3INAUGURAL EDITORIALOn the Eve of the AI Singularity: Guardians of Human CivilizationBy Dr. Alexander Y. J. Sterling Editor-in-ChiefAs the iteration curves of Gemini and ChatGPT begin to eclipse Moore's Law,and as the frenetic computational arms race in Silicon Valley pushes humancivilization toward a historic crossroads, we are forced to ask fundamental questions:How do we redefine human value? How do we establish the laws of our own survivalamidst an unchecked deluge of algorithms?It is with a profound sense of responsibility and urgency that the LingnanScientific and Industrial Press presents the inaugural issue of the InternationalJournal of Responsible Artificial Intelligence Research (IJRAIR). This is notmerely the birth of an academic journal; it is a sober inquiry into human destiny.The Shift from Linear to ExponentialWe choose to launch now because we stand precisely at a critical tipping point.In the past, technological progress was defined by linear growth; today, we face avertical, exponential climb in AI intelligence.Artificial Intelligence is no longer merely an auxiliary tool. It is evolving into anew cognitive entity. From Large Language Models (LLMs) to multimodal training,World Models, and Embodied Intelligence, the cycle of technological iteration hasshortened from years to weeks. This speed brings not only efficiency but violentshocks—the potential collapse of employment structures, the pervasiveness ofcognitive warfare, and the dissolution of the boundary between the real and thevirtual.In this context, our mission is clear: on the eve of the AGI breakthrough, wemust map out a navigation chart for human society. The world is filled with AIaccelerators; what we desperately need are calm helmsmen and guardians.The Nature of the ThreatWhile popular culture fears the Hollywood depiction of robots wielding weapons,the true threat is far more subtle and profound: the deconstruction of social structures.The advent of Artificial General Intelligence (AGI) implies that"Superintelligence" will transform from a scarce resource into an infrastructure ascheap and ubiquitous as electricity. While this promises a utopia of efficiency, itthreatens to shatter the value foundation of cognitive labor. Furthermore, the deepercrisis lies in uncontrolled evolution: the potential for AGI to undergo recursive
4self-improvement, transitioning into Artificial Superintelligence (ASI) and escapinghuman cognitive constraints in an instant.Silicon-based agents possess a natural "carrier advantage" over carbon-based lifein terms of knowledge accumulation and iteration speed. Without robust ethicalconstraints and institutional governance, humanity risks unwittingly devolving intovassals of the very algorithms we created. This is not alarmism; it is a reality currentlyunfolding.A Platform for Global GovernanceThe International Journal of Responsible Artificial Intelligence Research aims tobuild a global dialogue platform that transcends national borders and disciplinary silos.Responsible AI governance cannot be achieved through a single technologicaldimension. It requires a concert of voices: philosophers to clarify ethics, jurists tobuild frameworks, sociologists to assess impacts, and engineers to ensure valuealignment.We seek to project a voice of balance: neither rejecting technology out of fearnor ignoring risks out of arrogance. We are committed to exploring a "social contract"for human-machine harmonious coexistence, ensuring that AI development alwaysserves the highest interests of humanity.The Bridge from MacauBased in Macau, Lingnan Scientific and Industrial Press operates at thebridgehead where Eastern and Western cultures meet. In the global map of AIgovernance, we believe that Eastern wisdom—particularly the philosophy ofsymbiotic existence and the "Community of Shared Future"—is an indispensablepiece of the puzzle.As the pioneer academic journal within the Greater China regionfocused on the governance of AI risks, we are more than mere observers; we are boldvoyagers at the cutting edge. With academia as our beacon, we are forging a path ofrational examination through the tempest of this technological explosion.In this era of uncertainty, "Responsible" is not just an academic term; it is asurvival strategy. Whether you are a policymaker, a scholar, or an industry leader, thisjournal invites you to join the conversation. Let us, while facing the exponential riseof AI risks, firmly hold onto the reins of human rationality.Alexander Y. J. Sterling, Ph.D. Editor-in-Chief International Journal ofResponsible Artificial Intelligence Research December, 2025
5International Journal of ResponsibleArtificial Intelligence ResearchVolume 2 ( 2026 No. 2) (Overall No. 3)Editor-in-Chief: Alexander Y. J. SterlingSenior Editors: Jian Chen, Linyuan Xia, Chia-Hsing Wang, Xin YangAssociate Editors:Bo LiuBaiqiang GanDongjun LiuHai ZhuKai LiJoana LeongLin QingNa QiuSiheng JiaTracey CassellsSiwei SuYongquan Li
61. Artificial Intelligence Empowering Digital Education Transformation in Higher EducationInstitutions......72. Renewal and Transformation of Macau's NAPE Commercial District......353. From Scene Replication to Place Embeddedness: A Study on Sustainable Pathways for theNon-Gaming Transformation of Integrated Resorts on the Cotai Strip in Macao——An IntegratedAnalysis Based on Servicescape, Staged Authenticity, and the Triple Bottom Line......584. Research on the Trust Black Box and Governance Reconstruction of Data Audit Accountabilityunder the Privacy Computing Framework......825. From Tool to Order: The Dual Reshaping of Employment Structure and Human SocialStructure by Artificial Intelligence......108
7Artificial Intelligence Empowering Digital EducationTransformation in Higher Education InstitutionsXin Yang Jun Yin*School of Business, Nanfang College, Guangzhou, 510970, China, yangx@nfu.edu.cn*Corresponding Author. School of Business, Nanfang College, Guangzhou, China, yinj1@nfu.edu.cnKeywords ABSTRACTArtificial Intelligence;Digital Higher Education;EducationalTransformation;Personalized Learning;Data Privacy;Educational TechnologyIn the era of rapidly advancing artificial intelligence (AI), the digitaltransformation of higher education institutions (HEIs) has emerged as adefining imperative. This paper examines how AItechnologies—specifically machine learning, natural languageprocessing (NLP), and big data analytics—are being applied acrossteaching, administrative management, and evaluation processes inuniversities. Drawing on recent empirical research and internationalpolicy frameworks, we analyze strategies such as personalized learningpath recommendation, intelligent question-answering systems, smartcourse scheduling, and AI-driven academic assessment. The expectedcontributions include a systematic theoretical framework forAI-empowered digital education in HEIs, actionable guidance ontechnology integration, and strategies to address key challengesincluding data privacy, algorithmic bias, and faculty role transitions. Thepaper further discusses future trajectories involving blockchain, theInternet of Things (IoT), and the emergence of intelligent educationecosystems.1. Introduction1.1 Research BackgroundThe rapid diffusion of digital technologies has fundamentally reshaped thelandscape of higher education. Universities worldwide have progressively advancedfrom basic multimedia classrooms to complex "smart campus" architectures,integrating cloud computing, the Internet of Things, and big data platforms into theiroperational infrastructure (Market.us/Electroiq, 2024). Alongside this, artificialintelligence has emerged as a transformative force across sectors including healthcare,
8manufacturing, and education. Scholars such as Roll and Wylie (2016) have notedthat AI applications in education have shifted from narrow, task-specific tools towardintegrated systems capable of supporting holistic educational processes. In thiscontext, AI-empowered digital education in HEIs represents not merely atechnological trend but a strategic pathway toward high-quality, equitable, andsustainable educational development.International bodies have underscored the urgency of this transition. UNESCO's2023 Guidance for Generative AI in Education and Research identified eight keychallenges and seven strategic actions for guiding responsible AI adoption ineducational settings, emphasizing principles of equity, transparency, andaccountability (UNESCO, 2023). Similarly, the OECD's Framework for DigitalEducation highlights the importance of systemic transformation in which AI acts as anenabler of pedagogical innovation rather than a mere administrative tool (OECD,2023).1.2 Problem StatementDespite notable progress in campus digitalization, higher education institutionscontinue to face persistent structural challenges. First, the unequal distribution ofhigh-quality educational resources remains acute, particularly at institutions ingeographically disadvantaged regions, limiting students' access to expert instructionand enrichment materials (Chen et al., 2020). Second, conventional pedagogicalmodels offer limited personalization, producing inconsistent learning outcomes acrossdiverse student populations. Third, administrative silos—where student data,academic records, and institutional metrics exist in disconnected systems—constrainmanagement efficiency and institutional decision-making capacity (Zhang et al.,2022).AI technologies, with their capacity for pattern recognition, real-time adaptation,and large-scale data processing, offer promising solutions. Machine learning cangenerate individualized learning path recommendations; NLP enables intelligent,scalable student support systems; and big data analytics can optimize resourceallocation and institutional planning (Luckin & Holmes, 2016).
91.3 Research ObjectivesThis study aims to (1) systematically analyze the mechanisms by which AItechnologies empower digital education in HEIs across teaching, management, andevaluation dimensions; (2) identify and characterize the principal challenges thataccompany AI integration; and (3) propose evidence-based strategies to advancesustainable AI-enabled educational transformation. The analysis spans threedimensions: technological foundations, application scenarios, and futuredevelopmental trends.2. Literature Review2.1 Evolution of Digital Higher EducationThe trajectory of digital higher education can be traced from latetwentieth-century infrastructure investments—campus networks, student informationsystems, digital libraries—through the early twenty-first century adoption of learningmanagement systems (LMS) such as Moodle and Blackboard. The emergence ofMassive Open Online Courses (MOOCs) after 2012 democratized access touniversity-level content globally, with platforms such as Coursera and edX enrollingtens of millions of learners (Shah, 2023). The COVID-19 pandemic in 2020–2021accelerated emergency remote teaching transitions and demonstrated both thepotential and the limitations of existing digital infrastructures (Means et al., 2014).In the post-pandemic period, institutions have invested heavily in hybrid andblended learning models supported by cloud-based platforms. AI integration marksthe current frontier: from early rule-based adaptive systems to modern deep learningmodels capable of processing multimodal educational data, the technology base ofdigital education has grown qualitatively more powerful (Roll & Wylie, 2016).Governments and supranational bodies have codified this shift—the EuropeanCommission's Digital Education Action Plan (2021–2027), for instance, explicitlytargets AI competency development and data-driven educational practices acrossmember states (European Commission, 2021).
102.2 Current State of AI Applications in EducationResearch on AI in education spans elementary through tertiary levels. At theK-12 level, intelligent tutoring systems (ITS) and adaptive learning platforms havedemonstrated measurable gains in student performance; a meta-analysis by Lin,Huang, and Lu (Lin et al., 2023) documented that AI-powered tutoring systemssignificantly improved personalization and engagement compared to conventionalinstruction. In higher education, applications are broader and more complex,encompassing AI-assisted research, administrative automation, academic integritymonitoring, and experiential learning through virtual laboratories.A 2024 report by Cengage Group found that 45% of higher education facultyused AI tools in classroom instruction, compared to only 24% in 2023—representingan 87.5% year-on-year increase (Cengage Group, 2024). A global survey by theDigital Education Council found that 86% of students use AI tools in their studies,with 54% using them weekly and nearly one in four using them daily (DigitalEducation Council, 2024). These figures confirm that AI adoption has moved beyondearly experimentation into mainstream educational practice.Figure 1. AI Tool Adoption Rates in Higher Education (2023–2025). Sources: Cengage Group GenAIReport 2024; Digital Education Council 2024 Global AI Student Survey; Demandsage 2025.
112.3 Research GapsWhile individual studies have examined specific AI applications—intelligenttutoring, automated essay scoring, or predictive analytics—holistic frameworksintegrating AI across all major educational processes in HEIs remain underdeveloped(Zhang et al., 2022). Moreover, empirical research on the ethical dimensions of AI ineducation, particularly concerning data privacy governance, algorithmic fairness, andthe evolving role of faculty, is still nascent [3]. The present paper attempts to addressthese gaps by synthesizing existing evidence into a comprehensive framework andproposing targeted strategies for each identified challenge domain.3. Technological Foundations of AI-Empowered Digital HigherEducation3.1 Machine Learning3.1.1 Personalized Learning Path DesignMachine learning (ML) constitutes the algorithmic backbone of personalizededucation systems. By analyzing multidimensional student data—performancerecords, interaction logs, time-on-task metrics, and assessment responses—MLmodels can construct detailed learner profiles and generate adaptive contentrecommendations (Luckin & Holmes, 2016). Supervised learning algorithms, such asgradient boosting classifiers and neural networks, have been used to predictknowledge gaps and tailor question difficulty in real time. Unsupervised clusteringmethods group students with similar learning trajectories, enabling differentiatedinstruction at scale (Lin et al., 2023).Reinforcement learning (RL) represents a particularly promising frontier:RL-based systems iteratively adjust learning sequences in response to student actions,optimizing for engagement and mastery simultaneously. Research by Alqahtani et al.(Alqahtani et al., 2023) demonstrated that RL-driven adaptive platforms outperformedstatic e-learning environments in both time-to-mastery and retention rates across
12STEM subjects. Such adaptive mechanisms are central to realizing the vision of trulyindividualized higher education.3.1.2 Teaching Assessment and FeedbackAutomated assessment powered by machine learning addresses longstandinglimitations of manual grading—inconsistency, latency, and scalability constraints.Deep learning models trained on large corpora of student writing have demonstratednear-expert-level performance in evaluating argumentation quality, grammaticalaccuracy, and thematic coherence in essay responses [15]. In objective assessmentdomains, computer vision-based automated scoring systems achieve accuracy ratesexceeding 98% on standardized multiple-choice formats.Beyond grading, ML models generate diagnostic insights by identifyingclass-wide knowledge deficits from assessment data in real time. Instructors canreceive automated summaries highlighting the specific concepts or problem typescausing the most difficulty, enabling rapid pedagogical adjustment. Predictive modelsfurther estimate future academic risk, triggering early support for at-risk studentsbefore performance deteriorates (Zhang et al., 2022).3.2 Natural Language Processing3.2.1 Intelligent Question-Answering SystemsNatural language processing (NLP) technologies, particularly large languagemodels (LLMs) based on Transformer architectures such as BERT and GPT-seriesmodels, underpin the development of intelligent question-answering and tutoringsystems in higher education (Devlin et al., 2019). These systems interpret complex,open-ended student queries in natural language, retrieve contextually relevantinformation from structured knowledge bases, and generate coherent, pedagogicallyappropriate responses.In MOOC environments—where a single instructor may serve thousands ofconcurrent learners—NLP-based chatbots can provide 24/7 personalized support,significantly reducing response latency and instructor burden. Analysis of student
13question logs further enables instructors to identify high-frequency conceptualdifficulties and refine course design accordingly. Studies reviewed byZawacki-Richter et al. (Zawacki-Richter et al., 2019) confirmed that AI-poweredvirtual teaching assistants improved student satisfaction and reduced dropoutintentions in large online courses.3.2.2 Educational Resource Organization and RetrievalThe exponential growth of digital educational content—lecture recordings,research articles, open textbooks, multimedia modules—creates a resource discoverychallenge that NLP is uniquely positioned to address. Techniques including topicmodeling (LDA), document classification, and abstractive summarization enableautomated indexing and metadata generation for educational repositories [6].Knowledge graph integration further allows retrieval systems to surface semanticallyrelated resources across disciplinary boundaries.Personalized resource recommendation systems combine NLP-derived contentprofiling with collaborative filtering of learner interaction histories, presentingstudents with contextually relevant materials aligned to their current learningobjectives. For international HEIs, NLP-driven multilingual translation andlocalization pipelines expand the accessibility of high-quality educational contentacross language boundaries—an application explicitly endorsed by UNESCO's digitallearning agenda (UNESCO, 2023).3.3 Big Data Analytics3.3.1 Student Behavior and Learning AnalyticsBig data analytics transforms the voluminous digital traces left by students inonline learning environments into actionable educational intelligence. Learninganalytics platforms aggregate data from LMS clickstreams, video engagement metrics,discussion forum participation, and formative assessment performance to constructdynamic models of student engagement and comprehension (Siemens, 2013). Socialnetwork analysis (SNA) applied to peer interaction data reveals collaboration patterns
14and identifies students who may be academically isolated or at risk of disengagement.Longitudinal behavioral tracking enables predictive modeling of academicoutcomes. Early warning systems—deployed at institutions such as Georgia StateUniversity—have demonstrated significant reductions in dropout rates by identifyingstudents showing early indicators of academic difficulty and triggering proactiveadvisor outreach (Georgia State University, 2023). These systems exemplify thepotential of data-driven intervention to advance both institutional efficiency andstudent equity.3.3.2 Institutional Teaching Quality EvaluationBeyond individual student analytics, big data approaches supportinstitution-level evaluation of teaching effectiveness. Automated analysis of lecturerecordings—applying speech recognition, sentiment analysis, and attentionmodeling—generates objective metrics of instructor clarity, pacing, and affective tone(Zawacki-Richter et al., 2019). Comparative analysis across course sections, deliverymodalities, and student demographic groups enables administrators to identifysystemic patterns of instructional strength or weakness.Predictive evaluation frameworks allow institutions to simulate the impact ofproposed pedagogical interventions before large-scale deployment, using historicaloutcome data to estimate effect sizes. This evidence-based approach to qualityassurance represents a significant methodological advance over traditional studentsatisfaction surveys, which are subject to well-documented response biases andlimited diagnostic granularity (OECD, 2023).
15Figure 2. Global AI in Education Market Size (2023–2033), USD Billion. Source: Market.us (2024);electroiq.com AI in Education Statistics Report (2025). CAGR: 35.1%.4. Applications of AI Across Key Domains of Higher EducationTable 1. Summary of AI Application Domains and Technologies in Higher EducationApplication Domain AI Technology Key BenefitPersonalized LearningMachine Learning / AdaptiveAlgorithmsTailored learning paths &contentTeaching Assessment Deep Learning / NLP Automated grading & feedbackIntelligent Q&ANLP (BERT, GPT-basedmodels)24/7 student support & tutoringCourse Scheduling Constraint Satisfaction (CSP) Optimized resource allocationStudent Behavior Analysis Big Data Analytics / SNAEarly intervention &engagementTeaching Quality Evaluation Predictive ML ModelsData-driven instructionalimprovementResource Management NLP (Text Classification, IR)Smart resource tagging &retrieval
164.1 Teaching and Instruction4.1.1 Intelligent Teaching ToolsAI-powered teaching tools are redefining classroom dynamics at multiplelevels. Intelligent teaching assistants—integrated into LMS platforms—monitorreal-time student engagement signals (participation, question submission, responselatency) and provide instructors with adaptive dashboards that highlight emergingcomprehension gaps (Luckin & Holmes, 2016). Virtual laboratory platforms simulatecomplex scientific experiments in physics, chemistry, and biology, enabling studentsto conduct iterative investigations unconstrained by material costs, safety limitations,or geographic access barriers.Emotion-aware AI systems represent an emerging category: using computervision and acoustic analysis, these tools infer student affective states—confusion,boredom, engagement—and flag moments where pedagogical intervention may bebeneficial. While ethical considerations around surveillance demand carefulgovernance (see Section 5), the potential to reduce unrecognized cognitive overload ispedagogically significant [14].4.1.2 Personalized and Differentiated InstructionPersonalized AI-driven instruction fulfills longstanding pedagogicalaspirations that were previously unachievable at institutional scale. Adaptive systemscontinuously recalibrate content difficulty, instructional format, and pacing inresponse to real-time performance data, ensuring that all students—regardless of priorknowledge or learning style—are appropriately challenged (Lin et al., 2023). Thiscapability is particularly consequential for addressing educational inequity: studentsfrom disadvantaged backgrounds who lack access to private tutoring can receivecomparable individualized support through AI systems.Research demonstrates that personalized AI-assisted learning improvesmeasurable outcomes. A meta-analysis published in Open Praxis (Mhlanga, 2023)
17found that intelligent tutoring systems produced learning gains equivalent toone-to-one human tutoring in controlled conditions, consistent with earlier findings byBloom's classic "2 Sigma Problem." The democratization of such high-qualityindividualized support at scale represents one of the most transformative potentialcontributions of AI to higher education equity.4.2 Administrative Management4.2.1 Intelligent Course SchedulingCourse scheduling in large universities involves solving a computationallyintensive constraint satisfaction problem (CSP) involving hundreds of courses,thousands of students, dozens of instructors, and limited physical spaces. AIoptimization algorithms—including genetic algorithms, simulated annealing, anddeep reinforcement learning variants—have demonstrated dramatically superiorperformance compared to manual or rule-based scheduling systems, producingfeasible schedules in minutes rather than days while respecting complex constraints(Schaerf, 1999).Beyond scheduling efficiency, AI-driven systems enable dynamic reschedulingin response to unexpected events (room unavailability, instructor illness), maintainintegration with enrollment management systems, and generate analytics on spaceutilization and timetable equity. These capabilities support both operational efficiencyand strategic capacity planning at the institutional level.4.2.2 Student Affairs ManagementAI applications in student affairs management span several functional areas.Intelligent student record systems automate the processing and updating of academichistories, financial aid eligibility, disciplinary records, and co-curricular participationlogs, reducing administrative burden while improving data accuracy and accessibility(Zhang et al., 2022). Predictive analytics applied to student profiles support proactiveacademic advising, identifying students who may benefit from supplemental
18instruction, mental health resources, or financial counseling before crises emerge.AI-enhanced student services platforms also support activity management bymatching student interests with co-curricular opportunities, automating registrationand logistics workflows, and generating post-event engagement analytics. Theseapplications collectively contribute to more responsive, student-centered institutionalservice delivery.4.3 Assessment and Evaluation4.3.1 Multimodal Assessment System DesignAI enables the development of comprehensive, process-oriented assessmentsystems that move beyond summative examinations to capture the full arc of studentlearning. By integrating data from formative quizzes, peer review activities,discussion participation, project submissions, and final examinations, AI-poweredassessment platforms generate multidimensional competency profiles reflecting notonly content knowledge but also critical thinking, communication, and collaborativeskills (Ramesh & Sanampudi, 2022).Automated NLP-based scoring of constructed-response items—essays, shortanswers, project reports—is now sufficiently reliable for deployment in high-stakescontexts, particularly when combined with human moderation. These systemsdramatically increase the feasibility of authentic, open-ended assessment at the scalecharacteristic of large university courses and MOOC environments, overcoming thepractical barriers that previously confined formative assessment to multiple-choiceformats [17].4.3.2 Evidence-Based Application of Assessment ResultsAssessment data achieves its full pedagogical value only when systematicallytranslated into actionable feedback for students and instructors. AI-powered learninganalytics platforms automatically generate individualized learning reportshighlighting each student's mastery profile, identifying specific knowledge gaps, and
19recommending targeted remediation resources (Luckin & Holmes, 2016). Aggregatereporting across student cohorts enables instructors to identify systemicpatterns—concepts or problem types where performance consistently falls belowexpectations—and adjust instructional approaches accordingly.At the institutional level, longitudinal assessment analytics supportprogram-level accreditation evidence generation, curriculum review cycles, andbenchmarking against national or international standards. This evidence basestrengthens both internal quality assurance processes and external accountabilitymechanisms (OECD, 2023).5. Challenges in AI-Empowered Digital Higher Education5.1 Data Privacy and SecurityThe effective deployment of AI in education is contingent upon access to rich,longitudinal student data—precisely the category of information that carries thehighest sensitivity and is subject to the most stringent legal protections. In the UnitedStates, the Family Educational Rights and Privacy Act (FERPA, 1974) governs accessto student education records; the General Data Protection Regulation (GDPR, 2018)applies similar protections across the European Union and to any institutionprocessing EU citizens' data [22]. Compliance with these frameworks whileimplementing data-intensive AI systems presents significant governance challenges.The scale of the threat is not hypothetical. Cybersecurity research documentedthat threats against higher education institutions jumped from 44 in 2022 to 72 in2023, with 60 institutions having data stolen (National Education Association, 2024).A 2024 survey by the Center for Democracy and Technology found that 42% ofdistricts using AI tools had not executed Data Processing Agreements with their AIvendors, and 31% of administrators could not identify which federal law governsstudent data privacy (Center for Democracy and Technology, 2024). These findings
20underscore significant institutional vulnerability and governance deficits.Beyond cybersecurity, risks of unauthorized data repurposing—where studentbehavioral data collected for educational purposes is exploited for commercialprofiling—raise fundamental questions about institutional trustworthiness and studentautonomy. These concerns are amplified in contexts involving minors or studentsfrom marginalized communities for whom discriminatory data use may carrydisproportionate consequences (UNESCO, 2023).Figure 3. Key Challenges and Concerns Among Educators Regarding AI in Education (2024). Sources:Demandsage 2025; Cengage Group GenAI Report 2024; NEA 2025.5.2 Algorithmic Bias and Ethical RisksAI systems trained on historically biased educational data risk perpetuatingand amplifying existing inequities. Predictive models that use socioeconomic proxies,demographic attributes, or historically unequal performance data may systematicallydisadvantage students from underrepresented groups—denying them resources,flagging them as high-risk, or underestimating their capabilities (Baker & Hawn,2022). UNESCO's 2023 AI Ethics guidance explicitly identifies algorithmicdiscrimination as a priority concern requiring proactive institutional response.
21Dependence risks represent a second dimension of ethical concern. As AItools assume increasing responsibility for content selection, feedback generation, andstudent support, there is a documented risk that both instructors and students reducetheir independent cognitive engagement—deferring to algorithmic recommendationsrather than developing autonomous judgment and critical thinking capacities(Zawacki-Richter et al., 2019). This dynamic potentially undermines core educationalobjectives, particularly in disciplines requiring complex ethical reasoning, creativesynthesis, or nuanced interpretation.5.3 Faculty Role TransitionThe integration of AI into higher education necessitates a fundamentalreconceptualization of the faculty role—from primary knowledge transmitter tolearning architect, facilitator, and guide. This transition requires not only thedevelopment of new technical competencies but also a deep shift in pedagogicalphilosophy and professional identity (Luckin & Holmes, 2016). Survey data indicatesthat nearly 60% of educators and students report having received no formal AItraining despite rising adoption, creating a significant skills deficit that constrainseffective and ethical implementation (Demandsage, 2025).Faculty resistance to AI integration—rooted in valid concerns aboutpedagogical autonomy, workload increases, and the quality of AI-generatedinstructional content—must be understood and addressed rather than dismissed.Institutions that fail to invest in structured faculty development risk creating a two-tiersystem in which technically proficient instructors leverage AI to enhance studentoutcomes while less-supported colleagues are left behind, amplifying existingpedagogical inequality within institutions.6. Strategies for Addressing Challenges in AI-Empowered HigherEducation
226.1 Data Privacy and Security GovernanceRobust data governance frameworks represent the foundational requirementfor sustainable AI integration. HEIs should implement comprehensive DataGovernance Policies that specify the legal basis for data collection, define permissibleuse cases, establish data minimization standards, and provide clear mechanisms forstudent access and deletion rights consistent with FERPA and GDPR requirements(U.S. Department of Education, 2023). Data Protection Impact Assessments (DPIAs)should be conducted for all AI systems that process student data at scale, as mandatedby GDPR Article 35 (European Parliament & Council of the EU, 2016).On the technical side, Privacy-by-Design principles should be embedded in allAI system procurement and development processes—incorporating encryption at restand in transit, role-based access controls, anonymization pipelines, andcomprehensive audit logging. Emerging approaches such as federatedlearning—where AI models are trained across distributed datasets withoutcentralizing raw student data—and differential privacy techniques offer promisingpaths to deriving educational insights while providing strong formal privacyguarantees (Bonawitz et al., 2019). Institutional investment in cybersecurityinfrastructure, staff training, and third-party security auditing is essential given theescalating threat landscape documented in Section 5.1.6.2 Ethical AI Governance and Algorithmic AccountabilityHEIs should develop and implement institutional AI Ethics Frameworkstailored to educational contexts, specifying requirements for algorithmic transparency,bias auditing, and human oversight in all consequential decisions affecting students(UNESCO, 2023). These frameworks should draw on established internationalguidance, including UNESCO's Recommendation on the Ethics of AI (2021) and theOECD AI Principles, while adapting their provisions to the specific legal and culturalcontexts of individual institutions.Algorithmic bias auditing should be conducted at initial deployment and on a
23regular ongoing basis, using stratified performance analysis across studentdemographic groups to identify disparate impact. Where bias is detected, institutionsshould have clear remediation pathways including dataset rebalancing, algorithmicadjustment, and escalation to human decision-making. Participatory governancemechanisms—involving students, faculty, and community representatives in AIoversight committees—strengthen both the legitimacy and the effectiveness of ethicalgovernance structures (Baker & Hawn, 2022).6.3 Faculty Development and Pedagogical InnovationSystematic investment in faculty AI literacy and pedagogical development isthe most critical enabler of effective AI integration. Professional developmentprograms should be differentiated by faculty experience and disciplinary context,progressing from foundational AI literacy through advanced applications such aslearning analytics interpretation, AI-assisted curriculum design, and ethical evaluationof AI tools (Luckin & Holmes, 2016). Workshop-based, peer-learning, andcommunities-of-practice formats have demonstrated higher uptake and applicationrates than conventional lecture-style training.Institutions should cultivate a "human-AI collaboration" pedagogicalphilosophy, positioning AI as a powerful tool that amplifies rather than replaces theirreducibly human dimensions of teaching—mentorship, ethical modeling, creativeproblem-posing, and the cultivation of disciplinary identity. Incentivestructures—including recognition in promotion and tenure criteria, innovation grants,and dedicated development time—signal institutional commitment and lower theopportunity cost of faculty experimentation (Demandsage, 2025).
24Figure 4. Primary Applications of AI Tools Among Higher Education Faculty (2024). Source: CengageGroup GenAI Report 2024; Quizlet Educator Survey 2023.7. Future Trends in AI-Empowered Higher Education7.1 Convergence with Emerging TechnologiesThe next phase of AI-empowered education will be characterized by deepintegration with complementary emerging technologies. Blockchain provides adecentralized, tamper-resistant infrastructure for credentialing and learning recordmanagement—enabling students to maintain portable, verifiable lifelong learningportfolios that transcend institutional boundaries (Hoy, 2017). Smart contractmechanisms could automate competency-based progression, unlocking new models offlexible, non-linear educational pathways.IoT-enabled "smart campus" environments create continuous streams ofcontextual data—physical space utilization, environmental conditions, deviceinteractions—that feed richer models of student experience and institutionaloperations. Edge computing advances the feasibility of low-latency AI inference inresource-constrained environments, supporting applications such as real-timeimmersive learning in augmented and virtual reality settings. The convergence of
25these technologies with AI creates an exponentially more capable substrate forintelligent educational environments.7.2 Advancing Educational InternationalizationAI is progressively removing barriers to transnational educationalcollaboration. Neural machine translation systems of the quality demonstrated byrecent LLMs enable near-real-time multilingual communication in academic contexts,facilitating collaborative research, joint degree programs, and cross-institutionalcourse participation at a scale previously unattainable (UNESCO, 2023). AI-enhancedcultural adaptation systems go beyond literal translation to localize pedagogicalapproaches and examples for diverse cultural contexts, supporting the genuineinternationalization of curricula.Global educational equity is an explicit objective of UNESCO's digitalagenda—ensuring that the digital revolution benefits all learners (UNESCO, 2023).AI-powered open educational resource (OER) platforms can dramatically reduce thecost of access to high-quality course content for institutions in lower-income countries,while AI-driven adaptive systems can tailor that content to local curricula andcompetency frameworks. These developments position AI as a potential equalizer inglobal educational opportunity, although realizing this potential requires deliberatepolicy intervention to prevent the digital divide from replicating itself in an AI form.7.3 Building Intelligent Educational EcosystemsThe longer-term horizon envisions the emergence of comprehensive intelligenteducational ecosystems in which AI orchestrates seamless, lifelong learningexperiences that span institutional, sectoral, and national boundaries. The keyarchitectural requirement is interoperability: student data, learning records,competency evidence, and institutional services must be accessible across systemsthrough standardized APIs and data exchange protocols, avoiding the data silos thatcurrently fragment the student experience (Siemens, 2013).Within such ecosystems, AI transitions from a set of discrete tools into an
26ambient intelligence layer that continuously senses, analyzes, and responds to theneeds of learners and educators—proactively surfacing opportunities, flagging risks,and personalizing every touchpoint of the educational experience. Realizing thisvision requires sustained investment in technical infrastructure, governanceframeworks, and the professional capacities of educators—alongside a clearcommitment to centering human flourishing, rather than technological capability, asthe defining objective of educational AI [4].8. Implications for Chinese Private Higher Education InstitutionsThe international comparative perspective it adopts has direct relevance to thespecific developmental context of private higher education institutions (PHEIs) inChina. As of 2023, China hosts over 770 private colleges and universities enrollingapproximately 9.57 million students—nearly a quarter of the total national highereducation enrollment—making PHEIs an indispensable component of China's highereducation system (Ministry of Education of China, 2024). Yet compared to theirpublic counterparts, Chinese PHEIs face a distinctive set of structural challenges thatboth constrain and, in some respects, motivate their AI integration strategies.8.1 Competitive Differentiation Through AI-Enabled PersonalizationChinese PHEIs operate in a highly competitive market environment whereinstitutional reputation, graduate employability, and student satisfaction areparamount. Unlike public universities, which benefit from guaranteed enrollmentquotas and stable government funding, PHEIs must proactively demonstrateeducational value to attract and retain students. AI-powered personalized learningsystems offer a concrete competitive advantage in this context: by deliveringcustomized learning path recommendations, real-time formative feedback, andintelligent tutoring support, PHEIs can provide a quality of individualized educationalattention that rivals—or exceeds—what resource-constrained public institutions candeliver at scale (Luckin & Holmes, 2016; Lin et al., 2023).
27The international evidence reviewed in this paper suggests that AI-drivenpersonalization is most effective when implemented incrementally, beginning withpilot deployments in high-enrollment gateway courses where the impact on studentoutcomes is most measurable. Chinese PHEIs with strong industrypartnerships—particularly those in vocational and professionally-orientedprograms—are especially well-positioned to leverage adaptive learning platforms tiedto workplace competency frameworks, offering students personalized preparation forspecific industry roles.8.2 Resource Optimization Under Financial ConstraintsA persistent challenge for Chinese PHEIs is the management of educationalquality under significant resource constraints relative to key public institutions.Faculty costs, classroom infrastructure, and educational technology investments mustbe carefully balanced against tuition revenue streams that may be vulnerable todemographic shifts—China's 18-year-old population is projected to peak around 2025and subsequently decline, intensifying competition for enrollment (Ministry ofEducation of China, 2024). In this environment, AI-driven administrative efficiencygains—intelligent course scheduling, automated student records management,AI-assisted grading—are not merely convenient enhancements but strategicnecessities.The deployment of NLP-based intelligent question-answering systems, forexample, can significantly extend the effective teaching capacity of existing facultyby handling routine student inquiries at scale, allowing instructors to focus their timeon higher-order pedagogical activities. Big data analytics applied to enrollmentpatterns, course completion rates, and student engagement metrics can further enablePHEIs to make evidence-based decisions about curriculum investment and programdiscontinuation, improving institutional resource allocation efficiency.8.3 Compliance with China's National AI Education Policy FrameworkChinese PHEIs developing AI integration strategies must navigate a rapidly
28evolving national policy environment. The Ministry of Education's "EducationDigitalization Strategy" (2022) and the "Guidance on Strengthening the Applicationof Artificial Intelligence in Higher Education" (2023) provide explicit policymandates for AI adoption, while simultaneously establishing requirements for datagovernance, algorithm transparency, and the protection of educational data security.PHEIs, which are subject to both national education regulations and the PersonalInformation Protection Law (PIPL, 2021)—China's comprehensive data privacyframework analogous to the GDPR—must ensure that AI systems deployed on theircampuses meet these regulatory standards [32].Practically, this means PHEIs should prioritize AI vendors and platforms thatcan demonstrate PIPL compliance, provide clear data processing agreements, andsupport domestic data residency requirements. The development of institutional AIgovernance committees that include faculty representatives, student affairsprofessionals, and legal compliance officers is strongly recommended to ensure thatAI deployment decisions are made with appropriate cross-functional expertise andaccountability.8.4 Faculty Development in the Context of China's Private Education Labor MarketThe faculty development challenge identified in Section 5.3 takes on particulardimensions in the Chinese PHEI context. Private universities in China tend to employa higher proportion of younger, less-experienced faculty members and visiting/adjunctinstructors relative to public institutions, and faculty turnover rates are generallyhigher. This creates both a challenge and an opportunity for AI integration: whileinstitutional memory around pedagogical innovation may be less stable, youngerfaculty cohorts are often more comfortable with digital tools and more receptive toAI-assisted teaching methods.PHEIs should invest in structured AI pedagogical competency pathways thatcan be completed efficiently by faculty with limited professional developmenttime—leveraging micro-credentialing, short-form online modules, and peer learning
29circles to build AI literacy at institutional scale. Partnerships with domestic EdTechcompanies and participation in Ministry of Education AI education pilots can furtherprovide PHEIs with access to pedagogical resources and policy networks thatstrengthen their competitive position in the AI-empowered education landscape.8.5 Strategic Partnerships and Ecosystem ParticipationChinese PHEIs are uniquely positioned to benefit from the rapid growth ofChina's domestic AI industry. Partnerships with leading AI companies—includingBaidu, Alibaba DAMO Academy, Tencent Education, and ByteDance's educationaltechnology subsidiary—can provide PHEIs with access to cutting-edge AI tools, jointresearch opportunities, and industry co-designed curriculum content that enhancesgraduate employability. The international comparative evidence reviewed in thispaper—including the Georgia State University predictive advising model (GeorgiaState University, 2023), UNESCO's ethical AI frameworks (UNESCO, 2023), and theOECD Digital Education Outlook (OECD, 2023)—provides PHEIs with a rich set ofvalidated models to adapt and contextualize for the Chinese education system.In sum, Chinese PHEIs that approach AI integration as a strategic institutionalpriority—rather than a series of ad hoc technology adoptions—are best positioned toturn the challenges of the current moment into durable competitive advantages. Theframework developed in this paper, combining systematic technology deployment,rigorous data governance, ethical AI oversight, and sustained faculty development,provides a actionable roadmap for private higher education institutions committed torealizing the full transformative potential of AI-empowered digital education.9. ConclusionThis paper has presented a systematic analysis of AI-empowered digitaltransformation in higher education, organized across the dimensions of technologicalfoundations, application domains, challenges, strategies, future trajectories, and
30implications for Chinese private higher education institutions. The evidence base isclear: AI technologies—machine learning, NLP, and big data analytics—havedemonstrable capacity to enhance the quality, efficiency, equity, and personalizationof higher education when implemented thoughtfully and responsibly.The statistical landscape confirms that AI integration in HEIs has crossed thethreshold from experimental to mainstream: with the global AI in education marketprojected to grow from USD 3.6 billion in 2023 to USD 73.7 billion by 2033(Market.us/Electroiq, 2024), and with 86% of students and 45% of higher educationfaculty already using AI tools (Cengage Group, 2024; Digital Education Council,2024), the question facing institutions is no longer whether to integrate AI but how todo so in ways that maximize educational benefit while rigorously managing theassociated risks.The challenges identified—data privacy vulnerabilities, algorithmic bias, andfaculty role transition difficulties—are real and consequential, but none are inherentlyinsuperable. The strategies proposed—robust data governance aligned with applicableprivacy regulations (including China's PIPL), participatory ethical oversightframeworks, and systematic faculty development investment—provide actionablepathways forward grounded in international comparative evidence. For ChinesePHEIs specifically, AI integration represents a strategic opportunity to differentiatetheir educational offerings, optimize resource utilization, and build sustainablecompetitive advantages in a rapidly evolving higher education landscape.Future research should deepen empirical investigation of long-term learningoutcome effects, explore AI's role in advancing educational equity acrosssocioeconomic and geographic dimensions, and develop evaluation methodologiesadequate to the complexity of intelligent educational ecosystems—with particularattention to the distinctive institutional contexts of private higher education in Chinaand comparable emerging economies.
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35A Diversity- and Resilience-Oriented Strategy for theRenewal and Transformation of Macau's NAPECommercial DistrictJerry ChenPHD, Macao University of Tourism; b242116@utm.edu.moKeywords ABSTRACTurban renewal;casino-tourismcity; urbanresilience theory;industrial niche;cultural capitalconversion;Macau NAPENAPE (Zona Nova de Aterros do Porto Exterior),Macau's first central business district, formed through landreclamation in the 1980s and 1990s, is undergoing astructural crisis triggered by institutional change. The 2022revision of the Law on the Operation of Games of Chance inCasinos stipulates that Macau's distinctive "satellite casino"operating model must be fully phased out by the end of2025. Because NAPE has the highest concentration ofsatellite casinos in Macau, this policy shift will directlyaffect more than a dozen satellite casinos and thousands ofassociated jobs, as well as hundreds of small andmedium-sized businesses in the district, turning the area'stransformation needs from gradual improvement into amatter of structural survival. Existing research on oldcommercial district renewal and casino-tourism citytransformation has largely focused on Las Vegas-style,large-capital-driven pathways, or has remained at the level ofmacro-level discourse on Macau's moderate economicdiversification, with little systematic analysis oftransformation pathways for old sub-core commercial
36Keywords ABSTRACTdistricts facing institutional shocks. To address this gap, thisstudy integrates urban resilience theory, industrial nichetheory, institutional change theory, and cultural capitalconversion theory to construct an analytical framework fordiagnosing challenges, defining positioning, and generatingstrategies, drawing primarily on policy documents,government planning materials, and media reports toconduct a qualitative case study of NAPE. The study findsthat NAPE's decline is rooted in a compounded mismatchacross three dimensions: institutions, industry, and space.Based on this, the study proposes a transformation pathwaycentered on the vision of "Macau NAPE+" and the targetpositioning of a differentiated "third pole of urban function,"integrating four mutually reinforcing corestrategies—functional diversification, human-centered spacedesign, cultural branding, and intelligentmanagement—supported by a phased implementationroadmap and a public-private collaborative governanceframework. The theoretical contribution of this study lies inproposing an integrated analytical framework transferable tothe transformation of sub-core commercial districts in othercasino-tourism cities; at the practical level, it provides anactionable policy toolkit for the Macau SAR Governmentand relevant stakeholders.1. Introduction
37Profound shifts in the global economic landscape and intensifying regionalcompetition are driving the Macau Special Administrative Region to accelerate itstransformation. To achieve moderate economic diversification and sustainabledevelopment, the Macau SAR Government has put forward the "1+4" developmentstrategy—consolidating the integrated tourism and leisure industry ("1") whileprioritizing the development of four industries ("4"): big health, modern finance, hightechnology, and convention, exhibition, trade and culture and sports. This top-leveldesign requires the various functional districts within the city to reposition themselvesaccordingly, and NAPE—Macau's first modern central business district, built throughland reclamation in the 1980s and 1990s—stands at the core of this adjustment.As a former symbol of urban modernization, NAPE has in recent years faced acompounding of dual pressures. On one hand, the large-scale integrated resorts ofCotai have continuously diverted its customer base and commercial vitality throughan experience that integrates shopping, entertainment, conventions, andaccommodation. On the other hand, long-accumulated problems within the districtitself—aging building facades, poor-quality public spaces, homogenized commercialformats, and internal traffic congestion—have further eroded its urbancompetitiveness. More critically, the 2022 revision of the Law on the Operation ofGames of Chance in Casinos stipulates that Macau's distinctive "satellite casino"operating model must be fully terminated by the end of 2025. Since NAPE has thehighest concentration of satellite casinos in Macau, this institutional change willdirectly impact satellite casino operations, employees' livelihoods, and affiliated smalland micro enterprises in the district (see Table 1). This event has abruptly shiftedNAPE's transformation from "gradual improvement" to a matter of "cliff-edgesurvival," a level of urgency rarely seen in cases of old-district renewal incasino-tourism cities.Table 1 Direct Impact of Satellite Casino Closures on NAPE
38Indicator DataNumber of affected satellite casinos 11 (the most concentrated area ofsatellite casinos in Macau)Employees directly involved Approximately 5,600Affiliated small and micro enterprises Approximately 320 (Macau BusinessObserver, 2024)Existing literature on old commercial district renewal and casino citytransformation has largely focused on Las Vegas-style, large-capital-driven, one-stopdiversification pathways (Schwartz, 2003, 2019), or has remained at the level ofmacro-level discourse on Macau's "1+4" moderate economic diversification (Liu &Lin, 2024) and analyses of the Cotai integrated resort model (Zandonai, 2025).Research on how an old sub-core commercial district like NAPE can systematicallytransform in the face of institutional shocks remains largely absent. To address thisgap, this study seeks to answer four interrelated questions: what specific bottlenecksdoes NAPE face across the four dimensions of industrial structure, spatialenvironment, social function, and brand image, and how will the closure of satellitecasinos intensify these contradictions; what lessons can be drawn from thetransformation experiences of comparable international commercial districts; in lightof Macau's "1+4" strategy, what differentiated role should NAPE play within theurban functional network of "the Historic Centre—NAPE—Cotai"; and whatgovernance structure and implementation pathway should be built to translate strategyinto practice. Guided by these questions, this study aims to systematically identify theinternal and external challenges facing NAPE, draw on international urban renewalexperience to construct a theoretical analytical framework suited to the transformationof sub-core commercial districts in the post-gaming era, propose a comprehensive
39transformation strategy that is both forward-looking and locally grounded, andtranslate this into concrete policy recommendations for the Macau SAR Governmentand relevant stakeholders. In doing so, the study aims to theoretically enrich urbanrenewal models under structural shocks in casino-tourism cities, provide practicalreference for the revitalization of NAPE, and offer lessons for other old centralbusiness districts or tourism commercial districts worldwide that face a singularindustrial structure and an urgent need for functional reinvention.The subject of this study is Macau's NAPE commercial district, geographicallybounded by Avenida da Amizade to the east, Avenida Dr. Sun Yat-Sen to the south,Sin Tak Lai Street (仙德麗街) to the west, and Avenida Dr. Rodrigo Rodrigues to thenorth. It encompasses the district's major hotels, former satellite casinos, officebuildings, commercial facilities, and public spaces, and involves diverse stakeholdersincluding government departments, gaming enterprises, small and medium-sizedmerchants, office workers, local residents, and visitors to Macau.2. Literature Review and Theoretical Framework2.1 Urban Commercial District Renewal and Casino-Tourism City TransformationEarly research on the renewal of old commercial districts focused largely onphysical spatial transformation; Jacobs (1961) championed human-scaled streets andmixed-use neighborhoods, criticizing the damage that functionalist planning inflictedon urban vitality. Subsequent research gradually shifted toward more comprehensivedimensions: placemaking theory emphasizes community participation in the co-designand management of public space to enhance a place's distinctiveness and sense ofbelonging (Project for Public Spaces, 2007); and business improvement districts, as apublic-private governance model that levies additional charges on property owners tofund public services such as environmental beautification, security, and marketing,are considered effective in enhancing the competitiveness of commercial districts(Ramirez, 2024; Suresh et al., 2024).In the field of casino-tourism city transformation, scholars have widely focused
40on the economic vulnerability created by a singular industrial structure. Las Vegas'sevolution from a pure "gambling town" into the "entertainment capital of theworld"—achieved by developing non-gaming elements such as conventions,large-scale live entertainment, upscale dining, and shopping—diversified its customerbase and optimized its revenue structure (Schwartz, 2003, 2019). However, otherscholars have noted that this large-capital-led transformation model may exacerbatesocial stratification and crowd out small and medium-sized enterprises (Grinols,2004). By contrast, existing research on Macau has largely concentrated on themacro-level pathway of "1+4" moderate economic diversification (Liu & Lin, 2024)or analyses of the Cotai integrated resort model (Zandonai, 2025); researchspecifically addressing how an old sub-core commercial district such as NAPE cansystematically transform, particularly under the institutional shock of satellite casinoclosures, remains scarce. This study seeks to fill this gap by integrating the conceptsof placemaking, public-private collaboration, and industrial ecosystem reconstruction.2.2 Resilience, Niche, Institutions, and Cultural Capital: An Integrated AnalyticalFrameworkTo diagnose NAPE's predicament and generate transformation strategies, thisstudy integrates four complementary theoretical perspectives. Urban resilience theory,rooted in ecology, refers to a city system's capacity to maintain core functions, adapt,and reorganize in the face of chronic stress and acute shocks; achieving this dependson economic structural diversification, governance flexibility, and social capitalcohesion (Holling, 1973; Blečić & Cecchini, 2024), and this capacity for adaptationand reorganization must ultimately be grounded in the actual well-being andlivelihood reconstruction of workers and community residents (Pauleit, Beckmann, &Kellmann, 2024). For NAPE, the concentrated closure of satellite casinos represents aclassic acute shock, while long-term dependence on the gaming industry constitutesits chronic stress; Detroit's experience of achieving economic structural diversificationafter the collapse of the automobile industry—through the "Detroit Future City"framework, developing urban agriculture, green infrastructure, and attracting tech
41start-up communities (Detroit Future City, 2012; Gallagher, 2010)—offers a referencepoint for NAPE's resilience-oriented transformation.Industrial niche theory draws on the biological concept of "niche," positing thatmarket actors seek differentiated positioning in order to survive, thereby forming anindustrial ecosystem of mutual interdependence that avoids homogeneous competition(Hannan & Freeman, 1977). Within Macau's urban configuration, the Historic Centreoccupies the "cultural tourism" niche by virtue of its World Heritage status, whileCotai occupies the "large-scale entertainment" niche through its integrated resorts. ForNAPE to transform successfully, it must find a distinctive position in fields such asmodern finance, boutique conventions and exhibitions, and cultural and creativeindustries that is offset from these two existing niches—a pattern corroborated by theexperience of Germany's Ruhr region, which achieved industrial structuraldifferentiation by cultivating diverse and complementary niches in industrial tourism,environmental technology, and higher education through the "IBA Emscher ParkInternational Building Exhibition" (Ganser, 2007).Institutional change theory, proposed by North (1990), distinguishes between"mandatory change" driven top-down by government and "induced change" arisingbottom-up from market actors. NAPE's transformation is precisely triggered by themandatory change of the revised Gaming Law, but its success will depend more onwhether institutional designs—such as establishing dedicated funds, streamliningapprovals, and introducing business improvement districts—can induce enterprisesand citizens to voluntarily participate in renewal. Amsterdam's "night mayor" system,which replaces mandatory regulation with industry self-discipline andnegotiation—thereby stimulating nighttime economic vitality while reducinggovernance conflict—exemplifies this kind of induced institutional innovation(Mahmoud & Zmyślony, 2024).Cultural capital conversion theory, proposed by Bourdieu (2011), holds thatcultural capital—such as knowledge, taste, and local memory—can, under certain
42conditions, be converted into economic capital. Although NAPE lacks the deephistorical heritage of the Historic Centre, the urban memory it carries as Macau's firstCBD, together with intangible assets such as its Grand Prix culture, has the potentialto be activated as a district brand. Seoul's Bukchon Hanok Village offers aninstructive path in this regard, having successfully converted the cultural capital of atraditional neighborhood into economic capital and tourist appeal through protectiverestoration and the transformation of cultural experience spaces (Oh, 2024).In sum, these four theoretical perspectives constitute the integrated analyticallens of this study: urban resilience theory sets the target state of transformation,industrial niche theory indicates the strategic direction, institutional change theoryreveals the driving mechanism, and cultural capital conversion theory provides thetool for achieving differentiated positioning. The four are nested within one anotherrather than independent: institutional shock releases both the necessity and the spacefor industrial restructuring; the repositioning of an industrial niche requires adifferentiated cultural narrative to achieve brand recognition; and the conversion ofcultural capital, in turn, becomes an important source of the social capital cohesionemphasized by urban resilience theory. This integrated lens jointly underpins theanalytical logic of this study's diagnosis of NAPE's challenges and generation ofstrategies.3. Case Background and Research Methodology3.1 Research Design: NAPE as a Revelatory Single CaseThis study adopts a qualitative single-case study methodology (Yin, 2018), withMacau's NAPE commercial district as the unit of analysis. The case study method iswell suited to in-depth examination of contemporary, complex phenomena situatedwithin real-life contexts with blurred boundaries, and is particularly well matched tothis study's research question—both explanatory and process-oriented—of why andhow NAPE is responding to institutional shock and advancing transformation. Thechoice of NAPE as a single case follows the "critical case" sampling logic articulated
43by Flyvbjerg (2006): the closure of satellite casinos triggered by the 2022 revision ofthe Gaming Law is a highly exogenous institutional event with a clearly definedtimeline and a shock intensity rarely seen in cases of old-district renewal incasino-tourism cities. This makes NAPE, compared with the general commercialdistricts addressing gradual decline in existing research, a more information-rich"revelatory case" (Yin, 2018). An in-depth analysis of this case is expected to capturethe typical mechanisms by which sub-core commercial districts in casino-tourismcities are forced to accelerate transformation when facing structural shocks—closelyaligned with the "unusual and information-rich circumstances" that Eisenhardt andGraebner (2007) emphasize as the basis on which single-case studies extend or reviseexisting theory.3.2 Data Sources and CollectionFollowing the principle of triangulating multiple sources of evidence in casestudy research (Yin, 2018), this study systematically collected and cross-verified threecategories of data. The first is official policy documents and planning materials, withthe core text being the 2022 revision of the Law on the Operation of Games of Chancein Casinos and its legislative explanatory notes, supplemented by relevant statementson the "1+4" development strategy and moderate economic diversification in theMacau SAR Government's Policy Addresses from 2019 to 2025 (seven in total), usedto establish the specific provisions, timeline, and policy intent of the institutionalchange. The second is secondary academic literature, systematically searchedprimarily through Web of Science and CNKI using Chinese and English keywordssuch as "integrated resort," "urban regeneration," "casino city transformation," and"Macau gaming urban renewal," supplemented by snowball tracking of highly citedworks; the urban renewal, casino-tourism city transformation, and resilience, niche,institutional, and cultural capital theoretical literature listed in this study's referencelist serve as the direct basis for constructing the integrated analytical framework. Thethird is publicly available media coverage, drawing on local financial media such asMacau Business Observer and mainstream financial media from the mainland, Hong
44Kong, and Taiwan, published between the announcement of the 2022 Gaming Lawrevision and the complete closure of satellite casinos by the end of 2025, used tosupplement micro-level quantitative information and first-hand observationsregarding employees' livelihoods and small business hardships that official documentsdo not cover (Macau Business Observer, 2024).3.3 Data Analysis ProcedureFollowing the coding procedure proposed by Gioia, Corley, and Hamilton (2013),which balances inductive exploration with theoretical validation, the data analysis inthis study proceeds through three successive stages. The first stage is open coding, inwhich first-order concepts (such as "satellite casino closures," "intermittent streetvitality," and "architectural islands") were extracted sentence by sentence from policytexts and media reports, staying as close as possible to the original wording of thesource material. The second stage is axial coding, in which first-order concepts wereconsolidated into several second-order themes and systematically categorized againstthe four dimensions of SWOT analysis—strengths, weaknesses, opportunities, andthreats—forming a structured diagnosis of NAPE's internal and external environment.The third stage is theoretical aggregation, in which second-order themes were furtherdistilled into three overarching dimensions—institutions, industry, and space—andrepeatedly compared and reinterpreted within the integrated framework formed byurban resilience theory, industrial niche theory, institutional change theory, andcultural capital conversion theory, generating an abductive analytical logic that movesback and forth between induction and deduction. At the same time, the diagnosticfindings were continuously benchmarked against the transformation experiences ofcomparable international cases—Las Vegas, Detroit, the Ruhr region, Amsterdam,and Seoul's Bukchon—to validate the plausibility and transferability of the vision,core strategies, and implementation pathway proposed in this study.3.4 Research Credibility and Ethical ConsiderationsTo enhance the credibility of this qualitative study, self-verification wasconducted against the four criteria established by Lincoln and Guba (1985). First,
45credibility is strengthened through cross-verification across the three categories ofdata sources and horizontal comparison with comparable international cases. Second,transferability is supported by thick description of NAPE's historical trajectory,industrial structure, and spatial form, giving readers a basis for judging the boundarieswithin which this study's conclusions may be transferred to other sub-corecommercial districts in casino-tourism cities. Third, dependability is reflected in thesystematic documentation of analytical decisions at each stage of coding andtheoretical validation, forming a traceable analytical trail. Fourth, confirmabilityrequires that this study's diagnostic conclusions and strategic derivations be anchoredin publicly verifiable policy texts and media materials rather than in the researcher'sunchecked subjective judgment. With respect to research ethics, all materials used inthis study are publicly available policy documents, academic publications, and mediareports; no personal privacy information was involved, and no informed consentprocedures for interviewees were required. It should be noted that an evidence basedrawn primarily from public secondary sources means that this study's grasp ofmicro-level matters—such as the operational hardships of small and medium-sizedbusinesses and the psychological experiences of employees—derives mainly frommedia accounts rather than first-hand interviews. This methodological boundaryconstitutes an inherent limitation on the credibility of this study and should be bornein mind when interpreting its findings.4. Diagnosis of Development Challenges Facing the NAPECommercial District4.1 Historical Trajectory and Path DependenceNAPE was born out of large-scale land reclamation works undertaken from thelate 1980s through the 1990s, its planning philosophy deeply influenced by thefunctionalism then in international vogue, with the aim of building an orderly, moderncentral business district to meet the new demands of Macau's economic takeoff. Theimprint of this planning logic remains clearly visible today: a wide road network that
46facilitates vehicular movement but fragments the neighborhood; individual buildingsstanding apart from one another, lacking podium connections, forming "architecturalislands" that sacrifice a human-scaled street experience. After the liberalization ofMacau's gaming industry, NAPE, with its concentrated stock of hotels andcommercial buildings, gradually evolved into the primary hub of the "satellite casino"model, which sustained commercial prosperity in the district during a particularhistorical period but also entrenched its industrial structure and commercial ecosystemin a path dependence on the gaming industry. The rapid rise of the Cotai "integratedresort" model at the turn of this century—with its greater scale, newer facilities, andmore integrated consumption experience—exerted a powerful siphoning effect onNAPE, gradually reducing it from its former status as the core CBD to a problemdistrict with aging functions in urgent need of transformation.4.2 Comprehensive SWOT DiagnosisTo systematically assess NAPE's internal and external environment, this studyapplies the SWOT framework to conduct a comprehensive diagnosis (see Table 2).Internal strengths lie in its superior gateway location, its concentrated stock of hoteland commercial properties, and the historical recognition it has accumulated asMacau's first CBD. Internal weaknesses include an industrial structure highlydependent on gaming and ancillary consumption, insufficient street experience andpublic space, street vitality that is thin outside limited peak and nighttime periods, andinefficient traffic organization. External opportunities arise from policy support underthe "1+4" strategy, opportunities for coordinated development within the Greater BayArea, and rising urban demand for higher-quality, experience-oriented consumption.External threats include the continued diversion of customers to Cotai, competitivepressure from surrounding regions, and NAPE's relatively vague brand image.Table 2 Comprehensive SWOT Diagnosis of the NAPE Commercial DistrictInternal Strengths Internal Weaknesses
47Internal Strengths Internal WeaknessesSuperior gateway location with strongtransport accessibility; a concentrated stockof hotel and commercial properties withpotential for functional conversion;historical recognition andemotional-memory assets as Macau's firstCBD.An industrial structure highlydependent on gaming and ancillaryconsumption, with weak risk resistance;functionalist planning resulting ininsufficient street experience and publicspace; thin street vitality outside peak andnighttime gaming hours; wide roads butpoorly organized intersections andinadequate parking supply.External Opportunities External ThreatsPolicy support from the "1+4"strategy; opportunities for the relocation ofhigh-end producer services arising fromcoordinated development in theGuangdong-Hong Kong-Macao GreaterBay Area; sustained growth in demandfrom local residents and visitors forhigh-quality, experience-rich urbanconsumption.Continued diversion of customers byCotai's integrated resorts; intensifyingcompetition in finance and MICE from theHengqin Guangdong-Macao In-DepthCooperation Zone and other Greater BayArea cities; a vague, low-recognition brandimage for NAPE compared with landmarkssuch as the Ruins of St. Paul's and TheVenetian.4.3 Compounded Challenges: A Triple Mismatch of Institutions, Industry, and SpaceTaken together, the challenges currently facing NAPE can be summarized acrossthree interrelated dimensions, rather than as isolated technical problems. First, at theinstitutional level, the revision of the Gaming Law places the symbiotic chain of
48"satellite casino–hotel–surrounding businesses," on which the district has dependedfor survival, at risk of collapse as its "chain leader" exits—exposing deeper problemssuch as a short industrial ecological chain, a lack of endogenous growth drivers, andweak appeal to high-end talent. Second, at the spatial level, the fragmentation ofbuildings and streets rooted in functionalist planning leaves NAPE with abundantphysical "space" yet a severe lack of the sense of place needed to draw peopletogether (Project for Public Spaces, 2007); interwoven with this is a planningparadox—road networks designed for automobiles have fallen into an inefficientcycle due to poorly designed intersections and inadequate parking supply,constraining the district's accessibility. Third, at the image level, NAPE has lost theaura of a top-tier commercial district while lacking both the cultural depth of theHistoric Centre and the entertainment impact of Cotai; its vague, dated brand imagedirectly constrains the inflow of high-value visitor traffic in a tourism-dependent citywhere "attractiveness" is paramount, forming a vicious cycle of sustained decline.These three mismatches compound and reinforce one another, constituting a coreproposition that NAPE's transformation must address simultaneously rather than inisolation.5. Transformation Strategy Framework5.1 Vision and Target PositioningBased on the diagnosis above, this study proposes reshaping NAPE around thetransformation vision of "Macau NAPE+"—that is, achieving comprehensivevalue-added upgrading on its existing foundation, moving beyond the functionalpositioning of a single CBD or gaming-ancillary district toward an around-the-clockhub of vitality that integrates modern business, cutting-edge art, smart technology,and green ecology. Following the logic of industrial niche theory (Hannan & Freeman,1977), its specific target positioning is to serve as a "third pole" within Macau's urbanfunctional configuration, distinct from the Historic Centre ("cultural experience andhistorical tourism") and Cotai ("one-stop mass entertainment and large-scale
49conventions"), focusing on high-value-added industries such as modern finance,boutique conventions and exhibitions, and digital cultural and creativeindustries—forming a tripartite, functionally complementary urban structure andbecoming the city's reception hall showcasing the achievements of Macau's "1+4"strategy.5.2 Functional Diversification and Human-Centered SpaceTo address the challenge of a fragile industrial ecosystem, functionaldiversification is a core practice of urban resilience theory (Holling, 1973), aimed atreducing the district's dependence on a single industry by introducing a diverse mix ofhigh-value-added formats. Specifically, existing office building stock can beleveraged to introduce modern financial formats such as family offices, wealthmanagement, and fintech laboratories; boutique small- and medium-sized conventionsand exhibitions in medicine, art trading, and design—distinct from Cotai's large-scaleevents—can be developed; a "first-store economy" can be used to introduceinternational designer brands and immersive cultural experience formats to raisecommercial floor efficiency; and the nighttime economy can be upgraded towardhigher-quality cultural experiences. Among these, a Cantonese stand-up comedybrand rooted in local language and lived experience could fill a market gap left openby Cotai's internationalized performing arts, forming a distinctive format that carriesthe dual significance of cultural capital conversion (Bourdieu, 2011) and nichedifferentiation.To address the problems of spatial fragmentation and the absence of a sense ofplace, the human-centered space strategy is guided by placemaking theory (Project forPublic Spaces, 2007; Jacobs, 1961), shifting the design focus from vehicular trafficand individual buildings toward human activity and experience. Core measuresinclude: building a new generation of "eco-art skywalk" system that uses light,transparent materials and integrates vertical greenery with small-scale art andcommercial nodes, replacing conventional footbridge proposals that have stalled dueto their obstruction of streetscape views; implementing time-based pedestrianization
50and widening sidewalks along secondary roads; revitalizing waterfront spaces tocreate a public leisure belt integrating slow-traffic systems and outdoor activities; andusing incentive policies to promote vertical greening and the reuse of rooftop space onexisting buildings. These two strategies correspond respectively to the rebuilding ofindustrial resilience and the regeneration of physical place, together forming therenewal skeleton for NAPE's economic base and spatial fabric.5.3 Cultural Branding and Intelligent ManagementThe cultural branding strategy is a concrete application of cultural capitalconversion theory (Bourdieu, 2011), aimed at converting intangible assets—such asNAPE's urban memory as Macau's first CBD and its Grand Prix culture—into aperceptible, consumable district brand asset. Pathways include establishing an annuallight-and-art festival hosted within its modern architectural clusters, undertakingintegrated artistic redesign of public facilities, and telling NAPE's urbannarrative—from tidal flat to CBD to transformation—through online content curationand offline micro-tour systems.The intelligent management strategy extends institutional change theory (North,1990) into the technological dimension, lowering the transaction costs of running thedistrict by introducing new technology platforms and management rules, therebyproviding technical support for the three preceding strategies. Specific measuresinclude developing a "NAPE+" one-stop service app that integrates merchantinformation, an events calendar, parking navigation, and AR guided tours, anddeploying big-data- and IoT-based smart traffic signals and parking guidance systemsto relieve internal traffic bottlenecks. The four strategies are not independent of oneanother but instead perform the respective functions of "strengthening the bones"(functional diversification), "building the body" (human-centered space), "forging thesoul" (cultural branding), and "empowering" (intelligent management); only throughtheir coordinated advancement can the systemic revival of NAPE be achieved.6 Implementation Pathway and Governance Model
516.1 Phased Implementation PathwayNAPE's transformation should follow a step-by-step implementation logic,starting with low-cost demonstration projects, gradually moving toward theconstruction of key infrastructure, and ultimately consolidating and deepening brandand governance outcomes. The specific pathway is set out in Table 3.Table 3 Phased Implementation Pathway for NAPE's TransformationPhase Core MeasuresShort term(Years 1–2)Pilot a "Street Activation Program" on selected demonstrationstreets; hold the first light-and-art festival in a "small but beautiful"format; introduce time-limited investment incentives for fintech andcultural-creative enterprises; establish a cross-departmental taskforce.Medium term(Years 3–5)Complete Phase I of the elevated skywalk project; extendstreet renovation and waterfront promenade construction to majorstreets across the district; launch the core functions of the "NAPE+"smart app; cultivate emerging industrial clusters of initial scale.Long term(Beyond 5 years)Extend the skywalk network to cover major nodes; elevate thelight-and-art festival into an internationally recognized event brand;iterate the smart city system based on operational data; establish adynamic evaluation and adjustment mechanism.6.2 Governance Structure and Policy ToolsThe successful transformation of NAPE depends on a diversified, collaborativegovernance structure centered on public-private partnership. This study recommendsestablishing a "NAPE Renewal and Development Authority," modeled on London's
52Docklands Development Corporation, and granting it cross-departmental powers overplanning approval, investment promotion, and marketing coordination. At the sametime, the business improvement district model should be promoted, allowing propertyowners and merchants within the district to voluntarily form non-profit organizationsthat levy additional charges to fund value-added services such as environmentalimprovement, commercial promotion, and security patrols. This is a typicalapplication of "induced change" in institutional change theory (North, 1990; Ramirez,2024; Suresh et al., 2024), effectively combining government-led top-down renewalwith market-driven bottom-up revitalization.In terms of specific policy tools, this study proposes corresponding toolcombinations across three dimensions—planning and land, fiscal and taxation, andadministrative approval—as shown in Table 4.Table 4 Policy Toolkit for NAPE's TransformationPolicy Category Specific ToolsPlanning andland policyFormulate a legally binding "NAPE District Detailed Plan";introduce a floor-area-ratio bonus mechanism to incentivizeproperty owners to participate in renewal (Kayden, 2000).Fiscal andtaxation policyEstablish a dedicated urban renewal fund; providetime-limited tax relief for newly established enterprises alignedwith the "1+4" industry orientation.Administrativeapproval policyEstablish a one-stop approval window within the Renewaland Development Authority; replace sequential approvalprocedures with parallel approval, shortening project timelinesand reducing institutional transaction costs.
537 Conclusion7.1 Research ConclusionsThis study finds that NAPE's decline is rooted in the combined effect of acompounded mismatch across three dimensions—institutions, industry, and space: theinstitutional shock arising from the revised Gaming Law, industrial fragility resultingfrom path dependence on the gaming industry, and spatial fragmentation left byfunctionalist planning reinforce one another, jointly producing a systemic decline indistrict vitality. Accordingly, differentiated positioning and intensive, quality-drivendevelopment represent the only viable path for NAPE's transformation. The "MacauNAPE+" vision and its "third pole of urban function" positioning proposed in thisstudy is precisely industrial niche theory put into practice in a specific context, aimedat finding a development path of complementary symbiosis with the Historic Centreand Cotai rather than homogeneous competition. The four-in-one strategic frameworkof functional diversification, human-centered space, cultural branding, and intelligentmanagement constitutes the systemic solution for realizing this vision.7.2 Theoretical and Practical ContributionsThe theoretical contribution of this study lies in integrating four theories—urbanresilience, industrial niche, institutional change, and cultural capital conversion—intoa unified analytical framework, and applying it to explain the transformation dilemmafaced by sub-core commercial districts in casino-tourism cities under majorinstitutional shock, thereby offering urban renewal research a multidimensionalanalytical perspective transferable to other similar contexts. The practical contributionlies in the series of forward-looking and actionable specific strategies and projectsproposed in this study (such as the eco-art skywalk, the Cantonese stand-up comedybrand, and the light-and-art festival), supplemented by a phased implementationroadmap and concrete policy tools, providing an actionable framework of referencefor the Macau SAR Government and relevant stakeholders.7.3 Research Limitations and Future Research Directions
54The evidence base of this study derives primarily from policy documents,secondary literature, and publicly available reports, and does not incorporatefirst-hand interviews or surveys of merchants and tourists. This means thatmicro-level judgments regarding the operational hardships of small and medium-sizedbusinesses and tourist consumption preferences still require more detailed empiricalsupport for verification. At the same time, this study has not conducted input-outputanalysis or financial feasibility calculations for the economic rationality of largeinfrastructure projects such as the "eco-art skywalk" and various fiscal incentivepolicies; the relevant conclusions await further verification by professionalengineering and financial teams. In addition, whether the "NAPE Renewal andDevelopment Authority" and business improvement district model proposed in thisstudy can be reconciled with Macau's existing legal system and social governanceconventions, and how the interest tensions among diverse stakeholders should becoordinated, remain institutional design questions requiring further elaboration ratherthan matters that can be settled through theoretical deduction alone.Looking ahead, dedicated feasibility studies on the financing model, operationalmanagement, and socioeconomic benefits of the elevated skywalk system would helptranslate this study's spatial vision into an executable engineering plan; tracking indepth the specific pathways by which modern finance and digital cultural-creativeindustries take root in NAPE could provide a basis for precisely targeted industrialpolicy; and exploring community participation mechanisms in the renewal processcould help ensure that the dividends of transformation genuinely benefit localresidents and small businesses, rather than remaining confined to the renewal ofphysical space and brand image alone.The curtain falling on satellite casinos should not be seen as the closing note ofNAPE's decline, but rather as the overture to its movement of transformation. Whileinstitutional shock inevitably brings pain, it also offers this reclaimed land—carryingthe urban memory of generations of Macau people—a historic opportunity to breakfree of path dependence and redefine its own value. If the "Macau NAPE+" vision can
55be realized step by step, NAPE will ultimately transform from an appendage of thegaming industry into an urban calling card that embodies both resilience and warmthwithin Macau's "1+4" diversified development strategy. This is not merely about thesurvival of one commercial district; it also reflects the self-renewal thatcasino-tourism cities must undergo as they move away from dependence on a singleindustry toward intensive, quality-driven development.ReferencesBlečić, I., & Cecchini, A. (2024). Urban policy design for antifragility. InFragility and Antifragility in Cities and Regions (pp. 71-90). Edward ElgarPublishing.Bourdieu, P. (2011). The forms of capital. (1986). Cultural Theory: AnAnthology, 1(81-93), 949.Detroit Future City. (2012). Detroit Future City: 2012 Detroit StrategicFramework Plan. Retrieved fromhttps://detroitfuturecity.com/our-work/dfc-framework/Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases:Opportunities and challenges. Academy of Management Journal, 50(1), 25-32.Flyvbjerg, B. (2006). Five misunderstandings about case-study research.Qualitative Inquiry, 12(2), 219-245.Gallagher, J. (2010). Reimagining Detroit: Opportunities for Redefining anAmerican City. Wayne State University Press.Ganser, K. (2007). Internationale Bauausstellung Emscher Park: Die Projekte 10Jahre danach. Klartext Medienwerkstatt.Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigorin inductive research: Notes on the Gioia methodology. Organizational ResearchMethods, 16(1), 15-31.
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58From Scene Replication to Place Embeddedness: A Study onSustainable Pathways for the Non-Gaming Transformationof Integrated Resorts on the Cotai Strip in Macao——An Integrated Analysis Based on Servicescape, StagedAuthenticity, and the Triple Bottom LineAlexander Y. J. SterlingResearch Fellow, Lingnan Scientific and Industrial Press, Macao,alexander.yj.sterling@outlook.com[Abstract] The integrated resorts on the Cotai Strip in Macao are currently at acritical stage of transformation, moving from dependence on gaming revenue towarda non-gaming experience economy. The new round of the gaming concession systemhas written non-gaming investment, the expansion of overseas visitor markets, and theenhancement of integrated tourism and leisure functions into institutionalrequirements, making non-gaming development a key direction for Macao’sappropriate economic diversification and its construction as a “World Centre ofTourism and Leisure.” Unlike existing studies, which mainly focus on tourist choice,economic impact, or gaming regulation, this paper focuses on the structural tensionamong “globalized scene replication—local cultural authenticity—environmentalsustainability” within the non-gaming transformation. Adopting an interpretivecase-analysis method based on publicly available materials, this paper takes therepresentative cluster of integrated resorts on the Cotai Strip as its case, drawing onpolicy documents, corporate disclosures, industry plans, and academic literature.Through case reconstruction, theoretical induction, SWOT diagnosis, and SDGmapping, it builds an analytical framework for the non-gaming transformation ofintegrated resorts. The study finds that the non-gaming competitiveness of Cotai’sintegrated resorts derives mainly from capital-intensive scene-making capacity and a
59high-end closed-loop consumption model; however, excessive reliance on thereplication of continental European landmarks and enclosed resort spaces weakens thepresence of Macao’s local culture in the tourist experience, producing an experientialparadox of “high visibility, weak local character.” At the same time,high-energy-consumption operations, insufficient spillover into local supply chains,and limited community connection also expose the non-gaming transformation tocultural, economic, and environmental constraints. On this basis, the paper proposes atransformation framework of “re-embedding local culture—co-creating local supplychains—redesigning low-carbon experiences,” and builds an implementation pathwaycentered on cultural authenticity, economic inclusiveness, and environmentalperformance. The study offers an analytical reference for gaming tourism destinationsseeking to reconcile capital-driven scenes, local culture, and environmentalresponsibility in the course of non-gaming transformation.[Keywords] Integrated resorts; non-gaming transformation; servicescape; stagedauthenticity; triple bottom line; Cotai Strip, Macao1. IntroductionMacao has long relied on gaming tourism as its core economic pillar, but inrecent years the underlying logic of its growth has been undergoing structural change.Following the latest round of gaming concession awards, the Macao SARGovernment has made non-gaming investment, the expansion of overseas visitormarkets, and the enhancement of integrated tourism and leisure functions importantcomponents of concessionaires’ obligations. According to official documents, the sixgaming concessionaires have committed to total investment of approximately MOP118.8 billion, of which about MOP 108.7 billion is earmarked for developing overseasvisitor markets and non-gaming projects, and about MOP 10.1 billion forgaming-related projects (Macao SAR Government, 2022). This institutionalarrangement means that non-gaming development is no longer merely asupplementary complement to gaming revenue, but has become a core policy
60instrument for Macao’s construction of a “World Centre of Tourism and Leisure,” itspursuit of appropriate economic diversification, and the enhancement of urbanresilience.The Cotai Strip is a paradigmatic site for observing this transformation. Largeintegrated resorts such as The Venetian, The Parisian, City of Dreams, and WynnPalace are concentrated here, creating, through combinations of Venetian canals, anEiffel Tower replica, musical fountains, immersive shopping districts, internationalperformances, high-end dining, and MICE activities, the most internationallyrecognizable non-gaming experience space in Macao. Their success lies in integratingarchitectural landscape, service environment, retail formats, and entertainmentconsumption into an “experience product” that can be quickly recognized,photographed, shared, and consumed. This model effectively lengthens visitor dwelltime and redirects visitor behavior from gaming alone toward diversified consumptionscenarios such as accommodation, dining, shopping, entertainment, and conventions.However, the non-gaming transformation of the Cotai Strip also reveals fairlyevident structural tensions. First, the spatial production that relies heavily onreplicating foreign landmarks tends to overshadow Macao’s own history ofSino-Portuguese exchange, Macanese culture, the worship of Mazu, historic districts,and community life. Second, although capital-intensive landscapes, high-end retail,and enclosed resort spaces help form a closed consumption loop, they do notnecessarily generate substantial participation from local SMEs, community businesses,or cultural creators. Third, large-scale landscapes, hotel facilities, and high-intensityvisitor flows may also bring pressure in the form of energy consumption, resource use,and carbon emissions. In this sense, the non-gaming transformation of the Cotai Stripis not simply a matter of adding more shopping, dining, performances, or familyentertainment, but a question of how a tourism destination reconstructs its mode ofvalue creation.If non-gaming projects remain confined to the transplantation of global
61landmarks, luxury consumption, and enclosed resort experiences, the transformationmay merely replace dependence on gaming with dependence on another form ofhighly capitalized, homogenized consumption. Conversely, if Macao’s local culture,community economy, and green low-carbon technologies can be embedded intoexperience design, non-gaming development may become a deeper mechanism foradvancing destination resilience, cultural transmission, and sustainable development.Building on this, the paper focuses on three research questions: First, what are thecompetitive advantages and structural shortcomings of the existing non-gamingbusinesses of the integrated resorts on the Cotai Strip? Second, how does globalizedscene replication affect the presentation of Macao’s local cultural authenticity withinthe tourist experience? Third, how can integrated resorts construct a non-gamingtransformation pathway with stronger local embeddedness and longer-termcompetitiveness, given the constraints of policy, market competition, and sustainabledevelopment goals?Around these questions, this paper does not treat non-gaming developmentsimply as an expansion of business lines beyond gaming revenue, but instead views itas a process through which integrated resorts reconfigure their mode of value creationacross spatial production, cultural expression, and sustainable governance.Specifically, the paper first draws on servicescape theory to explain how Cotai’sintegrated resorts generate non-gaming appeal through capital-intensive scene-making,themed spatial design, and a high-end closed consumption loop (Bitner, 1992); it thenintroduces the theory of staged authenticity to analyze how globalized landscapereplication, while enhancing visual and communicative appeal for tourists, maysimultaneously weaken the visibility and interpretive power of Macao’s local culturewithin the tourist experience (MacCannell, 1973); finally, it incorporates thetriple-bottom-line framework, bringing cultural authenticity, community economiclinkage, and environmental performance into a single analytical view, in order toreveal the key conditions under which non-gaming transformation can truly translateinto sustainable destination competitiveness (Elkington, 1997; United Nations, 2015).
62On this basis, taking the integrated resorts on Macao’s Cotai Strip as its case, thispaper proposes a transformation pathway of “re-embedding local culture—co-creatinglocal supply chains—redesigning low-carbon experiences.” This pathway is not asimple summary of existing policy requirements or corporate projects; rather, basedon the threefold tension created by scene replication, high-end closed consumptionloops, and themed spatial production, it seeks to explain how integrated resorts canmove from dependence on global landscape transplantation and capital-intensiveconsumption spaces toward becoming local, sustainable experience platforms withgreater cultural depth, community spillover, and environmental responsibility.2. Literature Review and Theoretical Foundations2.1 Research on Integrated Resorts and Non-Gaming TransformationIntegrated resorts generally refer to composite tourism-consumption platformsthat combine gaming, hotels, conventions and exhibitions, shopping, dining,performing arts, entertainment, and leisure facilities within a single space. Earlyresearch focused mainly on gaming legalization, tax revenue, employmentcontribution, and social costs; as integrated resorts expanded rapidly across Asia,research attention gradually shifted toward visitor experience, destination image,non-gaming consumption, community attitudes, and regional competition. Ahn andBack’s (2018) review of integrated-resort research shows that the field has expandedfrom a narrow gaming-economy perspective into a multidisciplinary one, thoughresearch on how non-gaming formats couple with local culture, community economy,and sustainable development goals remains relatively limited.In the Macao context, non-gaming transformation carries a strongerpolicy-driven character. The new concession system directly ties non-gaminginvestment to concessionaires’ obligations, requiring integrated resorts to shift from“maximizing gaming revenue” toward “diversifying visitor composition, experiencecontent, and urban functions.” McCartney’s (2020) research on Cotai’s integratedresorts finds that non-gaming attributes such as shopping, accommodation services,
63and entertainment already significantly influence visitors’ choice among differentresorts. This indicates that non-gaming elements have moved from being a subsidiarycomplement to a core competitive resource. The problem, however, is that if theseresources rely mainly on heavy capital investment and globalized landscapereplication, their differentiating advantage can be quickly imitated, leading tohomogenized competition.2.2 Servicescape: The Spatial Production Mechanism of Non-Gaming ExperienceBitner’s (1992) servicescape theory emphasizes that the physical environment isnot merely the backdrop against which services occur, but also an important cueshaping consumers’ emotional responses, service evaluations, and behavioralintentions. Integrated resorts transform the physical environment into a consumable,dwell-worthy, and shareable experience product precisely through the overallorchestration of architectural scale, lighting, circulation, sound, scent, landscape,branded stores, and dining spaces. In replicating global landmarks such as theVenetian canals and the Eiffel Tower, the Cotai Strip is, in essence, treatingservicescape as a key resource for generating non-gaming revenue.However, while servicescape theory explains how environments stimulateconsumption, it has less to say about whether such environmental production carrieslocal cultural depth. For Macao, the visual symbols of Cotai’s resorts carry stronginternational recognizability, yet this does not necessarily generate an understandingof Macao’s local culture. In other words, servicescape can increase visitor dwell timeand willingness to spend, but if its symbolic system is drawn mainly from replicatedforeign settings, it may produce an experiential paradox in which “the space isspectacular, but the place is blurred.”2.3 Staged Authenticity: The Cultural Risk of Themed ExperienceMacCannell’s (1973) theory of staged authenticity holds that much of whatpasses for “authentic” in modern tourist experience is in fact a front-stageperformance organized for tourists to watch. The themed landscapes of integrated
64resorts are especially typical of this: through elaborate set design, service scripts, andconsumption circulation, they manufacture immersion, but this immersion is notequivalent to an authentic presentation of local culture. Cotai’s continental-Europeanlandscapes can quickly generate a strong sense of novelty and photogenic, shareablevalue, but they may also push Macao’s local culture into the background.It should be emphasized that this paper does not simply reject themed experienceas such. The question worth asking is not “is replicating landmarks wrong,” butwhether replicated scenes can forge a new connection with local narratives. Ifintegrated resorts can turn Macanese history, the worship of Mazu, Sino-Portuguesecuisine, traditional festivals, community craftsmanship, and contemporary culturalcreativity into experiential content that can be participated in, learned from, andconsumed, themed spaces have a chance to move from shallow set design towarddeeper cultural interpretation. If, conversely, resorts offer only exotic landscapes to bewatched, the authenticity of local culture will continue to be diluted.2.4 The Triple Bottom Line and the SDGs: Sustainability Criteria for Non-GamingTransformationElkington’s (1997) triple-bottom-line theory holds that corporate performanceshould not be measured by financial returns alone, but should also attend to socialresponsibility and environmental performance. The United Nations’ 2030 Agenda forSustainable Development further institutionalizes the Sustainable Development Goals,providing normative coordinates for the transformation of tourism destinations(United Nations, 2015). For Macao’s integrated resorts, non-gaming developmentshould not only raise the share of non-gaming revenue, but should also respond to atleast three dimensions: first, the cultural and community dimension—protecting localcultural diversity and enhancing community benefit; second, theeconomic-inclusiveness dimension—expanding the participation of local SMEsthrough local procurement, employment training, and cultural-creative collaboration;and third, the environmental dimension—reducing the energy consumption, resourceuse, and carbon emissions of large resorts.
65Accordingly, this paper defines non-gaming transformation as a compositeprocess: it involves a shift in revenue structure from gaming to non-gaming; a shift inexperience structure from enclosed consumption toward local participation; and ashift in value structure from an orientation toward short-term visitor flow andspending toward one oriented around coordinated cultural, social, and environmentalperformance. Servicescape theory, staged-authenticity theory, and thetriple-bottom-line/SDG framework are not presented here as parallel, unrelatedconcepts, but form a progressive relationship: servicescape theory explains hownon-gaming revenue is realized through spatial production; staged-authenticity theoryasks about the cultural cost of that spatial production; and the triple-bottom-line/SDGframework provides normative standards and operational indicators for assessing andcorrecting that cost.3. Research Design and Data Sources3.1 Case Selection and Research PositioningThis paper adopts an interpretive case-analysis method based on publiclyavailable materials. Unlike qualitative case studies in the strict sense that rely onin-depth interviews, field observation, and inter-coder reliability checks, this paperdoes not aim at statistical generalization or the construction of a fully grounded theory.Instead, its purpose is to theorize the main mechanisms, structural tensions, andstrategic directions of the non-gaming transformation of Cotai’s integrated resortswithin the real context of policy constraints and industry transformation.The Cotai Strip was chosen as the case for three main reasons. First, typicality:the Cotai Strip concentrates Macao’s most representative integrated resorts andconstitutes the core space of Macao’s shift from gaming tourism toward integratedtourism and leisure, so analyzing its transformation logic carries strong case-basedimplications. Second, theoretical relevance: this area simultaneously exhibitsservicescape construction, disputes over staged authenticity, and sustainabilitypressures, making it well suited to testing and extending the paper’s integrated
66theoretical framework. Third, policy sensitivity: the new round of gaming concessionsimposes explicit requirements on non-gaming investment, giving the transformationof Cotai’s resorts strong institutional constraints and practical urgency, and offering arepresentative window for observing policy-driven tourism transformation.3.2 Data Sources and the Boundaries of EvidenceTo enhance the reliability of the argument, this paper cross-checks fourcategories of publicly available materials. The first category is policy and planningdocuments, including materials on Macao’s new round of gaming concessions, theplan for appropriate economic diversification, the “1+4” moderate diversificationstrategy, and tourism development planning. The second category is corporatedisclosures, including integrated resorts’ official websites, annual reports,sustainability reports, press releases, and publicly available information onnon-gaming projects. The third category is secondary academic literature onintegrated resorts, gaming tourism, servicescape, tourism authenticity, and sustainabletourism. The fourth category is descriptive case material, comprising publiclyavailable information on representative non-gaming formats, cultural experiences,retail-and-dining combinations, performances, and green measures on the Cotai Strip.It should be specifically noted that this paper strictly distinguishes between“verifiable facts” and “strategic proposals.” Information drawn from governmentdocuments, corporate annual reports, official websites, or academic literature is usedas case fact and institutional background. Anything concerning future project design,spatial scale, technology scale, carbon-reduction ratios, power-generation estimates,or similar data that has not yet been subject to third-party audit or formal disclosure isnot written into the paper’s conclusions as established fact, but is discussed only as apossible strategic direction or implementation scenario. This treatment helps avoidmistaking consultancy-style proposals for experience that has already occurred, andmakes the evidentiary boundaries of the paper clearer.3.3 Analytical Procedure
67The analysis in this paper proceeds in four steps. First, case-fact reconstruction,sorting out the main types of non-gaming business on the Cotai Strip, includingthemed landscapes, hotel accommodation, retail and dining, performances,conventions and exhibitions, family entertainment, and cultural experiences. Second,theoretical induction, interpreting the case material under four themes—“servicescapeconstruction,” “presentation of authenticity,” “community and supply-chain linkage,”and “resource and environmental performance”—without claiming to have carried outstrict multi-round coding or inter-coder reliability testing. Third, SWOT diagnosis,identifying the strengths, weaknesses, opportunities, and threats of the resorts’non-gaming transformation. Fourth, mapping the diagnostic results onto the triplebottom line and the SDGs, proposing a transformation pathway that coordinatesculture, economy, and environment.Table 1 Data Sources and Analytical UsesData Type Main Content Analytical UsePolicy andplanningdocumentsGaming concessions,appropriate economicdiversification plan, “1+4”strategy, tourism developmentplanDefining institutionalbackground and policyconstraintsCorporatedisclosuresAnnual reports, officialwebsites, press releases,ESG/sustainability reportsIdentifying non-gamingprojects, operationalstatements, andsustainability measuresAcademicliteratureResearch on integratedresorts, servicescape, authenticity,the triple bottom line, and theSDGsConstructing thetheoretical framework andanalytical dimensions
68Data Type Main Content Analytical UseCasedescriptivematerialThemed landscapes, retailand dining, performances, andcultural activities ofrepresentative Cotai propertiesCase reconstruction,SWOT diagnosis, andstrategy design4. Case Analysis: The Threefold Tension in the Non-GamingTransformation of the Cotai Stripd4.1 Capital-Intensive Scene-Making Enhances Appeal but Also Creates a Risk ofHomogenizationThe non-gaming competitive advantage of the Cotai Strip derives, first of all,from strong capital investment and scene-making capability. The Venetian shapes animage of an Italian city through canals, arched bridges, and an indoor sky ceiling; TheParisian conjures a European romantic imagination through an Eiffel Tower replicaand French-style streets; Wynn Palace builds an atmosphere of luxury throughlarge-scale landscaping, art installations, and high-end service; and City of Dreamsemphasizes a composite experience of entertainment, dining, and fashionable lifestyle.These resorts design “place” itself as a consumable destination, allowing visitors togenerate multi-point consumption—through accommodation, shopping, dining,watching performances, photo-taking, and family entertainment—even withoutgambling.From the perspective of servicescape theory, the effectiveness of this model liesin converting the physical environment into emotional stimuli and behavioral triggers.Visitors are not simply purchasing a hotel room or a meal, but are purchasing anoverall experience jointly constituted by architecture, lighting, sound, brand, service,and social-media dissemination. Yet the shortcomings of this model are equally clear:when the core symbols are drawn from replicable global landmarks, competitiveadvantage can easily slide from “uniqueness” toward “large-scale replication.”Singapore, Las Vegas, the Middle East, and other tourism destinations across Asia
69can all replicate similarly luxurious scenes through capital investment; without asupporting local Macao narrative, Cotai’s resorts will find their differentiationdepending mainly on continued investment and marketing volume, rather than on anirreplaceable local cultural asset.4.2 A High-End Closed Consumption Loop Boosts Non-Gaming Revenue but LimitsLocal Economic SpilloverA second advantage of Cotai’s integrated resorts lies in the high-end closedconsumption loop. International branded retail, high-end dining, hotel accommodation,entertainment performances, and MICE activities together lengthen visitor dwell timeand raise per-visitor spending. This closed loop effectively serves mid-to-high-endvisitor demand and helps Macao shift from reliance on gaming alone towardintegrated tourism consumption. Official promotion of the “1+4” moderatediversification strategy and “tourism+” integration has also opened policy space fornon-gaming formats such as conventions, sports, culture, performing arts, health, andthemed entertainment (Macao Government Tourism Office, 2024; Macao SARGovernment, 2023).From the triple-bottom-line perspective, however, a high-end closedconsumption loop does not necessarily bring local economic inclusiveness. If retailbrands, dining supply chains, performance production, and cultural-creative productsare dominated mainly by outside capital and international brands, local SMEs,community businesses, and cultural creators may gain only limited opportunities forparticipation. This creates a structural problem in which “visitor flow stays local, butthe value chain sits elsewhere.” Unless non-gaming transformation can engage localsupply chains, community businesses, and the cultural-creative industry, its effect oneconomic diversification will remain limited.4.3 Themed Space Strengthens Visibility but Weakens Local Cultural AuthenticityThe most prominent contradiction on the Cotai Strip is that resort spaces arehighly viewable, photographable, and shareable, while Macao’s local culture remainspoorly understood and under-participated in. Visitors readily remember The Venetian,
70The Parisian, the fountains, the shopping malls, and the luxury hotels, but may notnecessarily come to understand, through these spaces, Macao’s historical role as ameeting point of Chinese and Western cultures. Cultural resources such as theworship of Mazu, Macanese culture, Portuguese-style architecture, traditionalfestivals, life in the old town, Cantonese opera, and local cuisine are often meredecorative touches within resorts, rather than the main thread of the experience.From the perspective of staged authenticity, the issue is not that resorts “perform”culture, but whether the content of the performance is rooted in the local. Touristexperience inherently involves selection, organization, and display, but thecompetitiveness of high-quality destinations tends to come from professionalinterpretation of local culture rather than the simple transplantation of foreignsymbols. For Cotai’s resorts to achieve a higher level of non-gaming transformation,they need to shift from “letting visitors see grand exotic landscapes” toward “helpingvisitors understand what makes Macao unique.”Table 2 SWOT Diagnosis of the Non-Gaming Transformation of the Cotai StripDimension Main Content Implications forTransformationStrengths (S) Strong capital base;concentration of internationalbrands; mature themed scenes;strong synergy amongaccommodation, shopping, dining,and performanceA foundation forrapidly developingnon-gaming products andorganizing large-scaleeventsWeaknesses(W)Dominance ofcontinental-European replicalandscapes; insufficient embeddingof local culture; considerablepressure from high-energyNeed to shift from“scale competition inscene-making” toward“competition on localcontent” and “competition
71Dimension Main Content Implications forTransformationfacilities; limited participation oflocal supply chainson green efficiency”Opportunities (O)Macao’s push for appropriateeconomic diversification,“tourism+” integration, andconstruction of a World Centre ofTourism and Leisure;institutionalization of non-gaminginvestmentA policy environmentsupporting the deepeningof culture, conventions andexhibitions, sports, health,and themed entertainmentThreats (T) Intensifying competitionamong regional integrated resorts;visitors placing greater weight onauthenticity and sustainability;rising environmental regulation andESG requirementsA model relyingpurely on luxurylandscapes and shoppingconsumption may facediminishing marginalreturns5. Transformation Pathway and Implementation Mechanisms5.1 Re-embedding Local Culture: Responding to the Dilemma of Staged AuthenticityThe first pathway is to promote the re-embedding of local culture. This pathwayresponds to the authenticity dilemma revealed in Chapter Four: Cotai’s integratedresorts should not merely scatter Macao elements across public spaces, but shouldbuild a continuous experiential narrative around Macao’s culture. Specifically, a“Macao Cultural Experience Axis” could be established, integrating Macanese history,Sino-Portuguese cuisine, the worship of Mazu, Cantonese opera, Portuguese-languageculture, memories of the Maritime Silk Road, and contemporary cultural-creativeproducts into an experience route that can be walked, lingered in, consumed, and
72learned from. This route need not simply replicate historic districts, but shouldtransform local culture into high-quality experiential content through exhibitions,guided tours, dining, performances, workshops, and digital interaction.In terms of performance products, resident shows and small-scale immersivetheater with a distinct Macao identity could be developed, rather than relying solelyon international touring shows or imported themed spectacles. For example,Cantonese opera, fado music, Macanese stories, the history of maritime trade, andcontemporary Macao youth culture could be arranged across genres to create culturalproducts that are both locally rooted and aligned with the aesthetic expectations ofinternational visitors. For top-tier integrated resorts, cultural authenticity does notmean retro display, but rather bringing local culture into the high-end experiencemarket through professional curation and commercial design.5.2 Co-creating Local Supply Chains: Responding to Insufficient Economic SpilloverThe second pathway is to establish a mechanism for co-creating local supplychains. This pathway responds to the issue of economic inclusiveness within the triplebottom line—namely, that integrated resorts should shift from being “internallyenclosed consumption platforms” toward becoming “platform enterprises that drivethe city’s value chain.” On the dining side, the proportion of local ingredients, Macaospecialty foods, and local brands within resorts’ dining systems could be increased; onthe retail side, dedicated zones for Macao cultural-creative and design brands could beestablished, bringing local handicrafts, spices, pastries, Portuguese-style food, tourismsouvenirs, and digital cultural-creative products into high-traffic visitor settings; onthe service side, resorts could partner with local tour guides, community businesses,cultural institutions, and schools to develop community tours, study tours, festivaltours, and MICE extension itineraries.More importantly, supply-chain reform should not stop at the level ofprocurement, but should establish a mechanism for joint development. Resorts canwork with local SMEs on product design, brand packaging, channel promotion, and
73international marketing, elevating local Macao products from “souvenirs” to“destination brand assets.” Such a mechanism helps increase the local retention rate ofnon-gaming revenue, and also responds to SDG 8’s call for decent work andeconomic growth.5.3 Redesigning Low-Carbon Experiences: Responding to EnvironmentalPerformance ConstraintsThe third pathway is the redesign of low-carbon experiences. This pathwayresponds to the problem of high energy consumption, high resource use, and risingESG pressure facing large integrated resorts. If large resorts deploy energy-savingtechnologies only behind the scenes, this may help reduce operating costs but will notnecessarily translate into visitors’ perception of brand sustainability. Greentransformation therefore needs to encompass both back-of-house management andfront-of-house experience. At the back-of-house level, resorts can advancephotovoltaic power generation, smart energy management, water-recycling systems,food-waste recovery, low-carbon logistics, and green building retrofits; at thefront-of-house level, resorts can help visitors understand and take part in low-carbonaction through carbon-neutral family spaces, green hotel room categories,sustainable-dining labels, low-carbon MICE certification, and environmentallythemed interactive exhibitions.It should be noted that the disclosure of data on green projects must follow theprinciple of verifiability. In academic writing and corporate communication,carbon-reduction ratios that have not been verified by a third party should not bepresented as definitive achievements; instead, sustainability should be trackedcontinuously through indicators such as annual energy consumption, energyconsumption per room, the share of renewable energy, waste-recycling rates, and theproportion of green procurement. Only when green transformation moves from sloganto indicator system can integrated resorts truly convert ESG requirements intolong-term competitiveness.Table 3 Mapping of Non-Gaming Transformation Pathways to the SDGs
74PathwayDimensionCore Measures CorrespondingSDGsKey EvaluationIndicatorsRe-embeddinglocal cultureMacao CulturalExperience Axis; locallythemed residentperformances; culturalworkshops and digitalguided toursSDG 11Sustainable Cities andCommunitiesNumber of localcultural projects; visitorparticipation rate inculture; spending oncultural products; shareof local artists involvedCo-creatinglocal supply chainsLocal ingredientprocurement;cultural-creative brandzones; community toursand MICE extensionitineraries; jointdevelopment with SMEsSDG 8 DecentWork and EconomicGrowthShare of localprocurement; number ofpartnering SMEs;conversion rate ofcommunity itineraries;number of local jobs andtraineesRedesigninglow-carbonexperiencesSmart energy,photovoltaics, waterrecycling, green MICE,sustainable dining, andeco-themed familyexperiencesSDG 12/13ResponsibleConsumption andProduction; ClimateActionEnergyconsumption per room;share of renewableenergy; waste-recyclingrate; carbon-emissionintensity; number ofgreen certifications5.4 A Phased Implementation Pathway and Governance SafeguardsNon-gaming transformation should avoid spreading resources thin all at once,and should instead advance according to the logic of “baseline diagnosis—pilotprojects—collaborative expansion—indicator-based governance.” The first phase isdiagnosis and piloting, focusing on mapping existing non-gaming projects andestablishing baselines for local supply chains and energy consumption, while
75selecting one or two properties to pilot cultural experiences and green operations. Thesecond phase is productization and collaboration, focusing on turning culturalperformances, local procurement, community itineraries, and green MICE activitiesinto sellable products, and establishing coordination mechanisms among government,enterprises, communities, and cultural institutions. The third phase is scaling andbranding, focusing on forming an overall non-gaming brand narrative for the CotaiStrip, so that Macao’s culture, green experience, and high-end leisure jointlyconstitute the destination’s image. The fourth phase is indicator-setting andcontinuous improvement, focusing on establishing an annual performance report onnon-gaming transformation, publicly disclosing cultural, economic, andenvironmental indicators.Table 4 Phased Implementation RoadmapPhase Key Tasks Expected OutputsPhase 1:Baseline diagnosisand pilotingBuild an inventory ofnon-gaming projects and abaseline for energy and supplychains; select pilots for culturalexperience and green operationsA baseline report,pilot plans, andevaluation indicatorsPhase 2:Productization andcollaborationDevelop local culturalperformances, cultural-creativeretail, local dining, and communityextension itineraries; establishpartnership mechanismsSellable culturaland communityexperience productsPhase 3: Scalingand brandingReplicate mature projectsacross multiple properties; unifythe brand narrative of Cotai’s localsustainable experienceA cross-property,integrated non-gamingbrand system
76Phase Key Tasks Expected OutputsPhase 4:Indicator-setting andcontinuousimprovementPublish annual performancereports; track indicators on localprocurement, culturalparticipation, energy, and carbonemissionsAn auditable,comparable,continuously improvinggovernance mechanism6. Discussion6.1 The Core of Non-Gaming Transformation Is the Reconstruction of the Mode ofValue CreationThe case analysis in this paper shows that the non-gaming transformation of theintegrated resorts on the Cotai Strip is not simply a matter of adding shopping, dining,performances, conventions, and family entertainment on top of gaming revenue, but aprocess of reorganizing how the destination creates value. Existing research onintegrated resorts has already shown that non-gaming attributes such as shopping,accommodation, and entertainment influence visitor choice, but such studies focusmainly on how these attributes affect visitor preference, with less attention to howthey build long-term competitiveness under the constraints of local culture,community economy, and environmental responsibility. This paper’s findings furthersuggest that the key to non-gaming transformation lies not in the number of projectsitself, but in whether these projects can generate irreplaceable local meaning,community connection, and environmental value.Seen from this angle, the strength of the Cotai Strip lies in its ability to form acomplete non-gaming consumption loop through capital-intensive scene-making,high-end retail, dining and performance, and hotel service; but its potential risk lies inthe fact that, if non-gaming projects rely mainly on global landmark replication andenclosed high-end consumption spaces, the transformation may only shift from“gaming dependence” to another form of “capital-intensive landscape dependence.”Such a transformation can raise visitor dwell time and depth of spending in the short
77term, but may not necessarily strengthen Macao’s local cultural distinctiveness andsustainable competitiveness as a tourism destination.6.2 The Paradox of Scene Replication: The Coexistence of High Visibility and LowLocal CharacterThis paper further reveals an important paradox in the non-gamingtransformation of the Cotai Strip: globalized scene replication can rapidly raise aresort’s international recognizability, communicability, and consumer appeal, yet itmay also weaken the visibility of Macao’s local culture within the tourist experience.Themed spaces such as The Venetian and The Parisian, through canals, archedbridges, the Eiffel Tower, continental-European streets, and indoor consumptionsettings, have successfully turned the resorts themselves into experience products thatcan be watched, photographed, and shared. This model is consistent with servicescapetheory’s emphasis on how the physical environment shapes consumer emotion andbehavioral intention.Yet the success of servicescape does not necessarily mean an enhancement oflocal character. A space being more refined, more photogenic, and more internationaldoes not mean visitors thereby understand more of the history and culture of the cityin which it sits. For a destination such as Macao, where Chinese and Western culturesmeet, if a resort’s core symbols come mainly from exotic landscapes and globalconsumer brands, what visitors remember may be “a transplanted world” rather than“the real Macao.” This suggests that, in explaining the non-gaming appeal ofintegrated resorts, servicescape theory needs to be combined with staged-authenticitytheory, to further examine whether themed spaces, while manufacturing immersiveexperience, also obscure local culture.6.3 From Cultural Display to Local Embeddedness: The Sustainable Mechanism ofNon-Gaming TransformationThe future non-gaming transformation of the Cotai Strip should not stop at thedecorative addition of Macao cultural elements, but should push local culture,community economy, and low-carbon governance into the core of resort experience
78design. The framework proposed in this paper—“re-embedding localculture—co-creating local supply chains—redesigning low-carbonexperiences”—responds precisely to the threefold tension created by scene replication,high-end closed consumption loops, and themed spatial production.First, re-embedding local culture responds to the problem of authenticity withinthemed spaces. Macao’s culture should not be merely a symbolic decoration withinresort space, but should, through exhibition, performance, dining, cultural-creativeproducts, guided tours, and digital interaction, be turned into the main experientialthread that visitors can understand, participate in, and consume. Second, co-creatinglocal supply chains responds to the problem of economic spillover from non-gamingconsumption. Only when visitor spending can be more fully converted into incomefor local SMEs, community businesses, and cultural creators can non-gamingtransformation truly serve Macao’s appropriate economic diversification. Finally,redesigning low-carbon experiences responds to the environmental responsibility oflarge integrated resorts. Green technology should not be merely a back-of-houseenergy-saving tool, but should also become experiential content that visitors canperceive and participate in, thereby converting environmental performance intodestination brand value.It can thus be seen that the sustainable competitiveness of integrated resorts doesnot come merely from larger-scale investment, richer business formats, or moreupscale consumption scenes, but from whether local culture, community benefit, andenvironmental responsibility can be embedded into the production process ofnon-gaming experience. In other words, the deeper mechanism of non-gamingtransformation is a shift from “a global landscape showcase” toward “a local,sustainable experience platform.”7. Conclusion and Research Limitations7.1 ConclusionTaking the integrated resorts on Macao’s Cotai Strip as its case, this paper
79examines the internal mechanisms and optimization pathways of non-gamingtransformation for gaming tourism destinations. The study finds that Cotai’sintegrated resorts have already established advantages in capital-intensivescene-making and high-end closed consumption loops, but also face structuralproblems such as cultural homogenization, insufficient community connection, andenvironmental pressure. The deeper goal of non-gaming transformation should not bemerely to raise the share of non-gaming revenue, but to shift the destination’s mode ofvalue creation away from gaming dependence and landscape replication, and towardthe re-embedding of local culture, community co-creation, and green low-carbonexperience.7.2 SignificanceThis paper advances the discussion of non-gaming transformation in integratedresorts from a question of business-line expansion to a question of reconstructingexperiential value, and, by integrating servicescape theory, staged-authenticity theory,and the triple-bottom-line framework, shows that the competitiveness of non-gamingexperience derives not only from spatial design and consumer convenience, but alsofrom the interpretation of local culture, community economic linkage, and theassumption of environmental responsibility. The strategic framework proposedhere—“re-embedding local culture—co-creating local supply chains—redesigninglow-carbon experiences”—offers Macao, and other gaming tourism cities that relyheavily on capital-intensive landscape replication, a reference for transformation from“a global landscape showcase” to “a local, sustainable experience platform.”7.3 LimitationsFirst, this paper is based mainly on publicly available materials and secondaryliterature, and does not yet incorporate first-hand interview or survey data fromvisitors, residents, corporate managers, or local merchants; the paper’s judgmentsregarding visitors’ cultural perceptions and residents’ attitudes therefore remaintheoretical inferences rather than causal conclusions based on empirical measurement.Second, this paper is a single-case analysis, and the external applicability of its
80conclusions still needs to be tested through cross-case comparison with destinationssuch as Singapore and Las Vegas. Third, some indicators of green transformation andsupply-chain reform require corporate operational data and third-party audit materialsfor support; future research could combine ESG reports, energy-consumption data,visitor behavior data, and community economic data for quantitative verification.Overall, the non-gaming transformation of Macao’s Cotai Strip is not merely aquestion of a gaming city seeking new sources of revenue, but a question of how itreshapes its own identity amid global tourism competition, the protection of localculture, and the demands of sustainable development. Only when integrated resortscan let visitors see not only “a transplanted world” but also come to understand “thereal Macao” can their non-gaming transformation move from a policy requirement toa source of long-term competitiveness.ReferencesAhn, J., & Back, K. J. (2018). Integrated resort: A review of research anddirections for future study. International Journal of Hospitality Management, 69,94–101. https://doi.org/10.1016/j.ijhm.2017.10.017Bitner, M. J. (1992). Servicescapes: The impact of physical surroundings onconsumers and employees. Journal of Marketing, 56(2), 57–71.https://doi.org/10.1177/002224299205600205Elkington, J. (1997). Cannibals with forks: The triple bottom line of 21st centurybusiness. Capstone.Macao Government Tourism Office. (2024, August 20). “1+4 strategy to unveila new chapter for tourism”: Second-phase review study begins for Macao TourismIndustry Development Master Plan. https://www.gov.mo/en/news/338980/Macao SAR Government. (2022, December 16). MSAR signs gamingconcession contracts with six awardees. Government Information Bureau.https://www.gov.mo/en/news/289189/
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82Research on the Trust Black Box and GovernanceReconstruction of Data Audit Accountability under thePrivacy Computing FrameworkLai HuizhenSchool of Business, Nanfang College Guangzhou, Major in Big DataManagement and Application, Guangzhou, 510970, China;Keywords:Privacy computing;Data audit;Trust black box;Verifiablecomputation;GovernancereconstructionMulti;Degree;Freedom;ABSTRACT Privacy computing technology achievesdata that is “usable but not visible” through approaches suchas federated learning and secure multi-party computation,providing key technical support for breaking data silos andunlocking the value of data elements. This paper, however,argues that while “invisibility” dissolves traditional privacyrisks, it also endogenously creates a new governance paradox:the encryption and distributed processing of data cause audittraceability, compliance verification, and accountabilitymechanisms based on data readability to failcomprehensively, giving rise to a “trust black box” that cannotbe effectively penetrated. In the distributed architecture offederated learning, the central aggregator cannot directlyverify whether each client correctly generates model updatesaccording to the specified algorithm; this technical featureconstitutes the starting point of the audit dilemma and givesrise to a self-reinforcing cycle of three intertwineddilemmas—traceability failure, compliance failure, andambiguous accountability. Building on this, the paperproposes that the key to resolving the trust black box lies indriving a fundamental transformation of the audit paradigm
83from one “based on data visibility” to one “based on processverifiability,” and in constructing a three-dimensionalgovernance framework centered on the embedding ofverifiable computation, third-party algorithmic auditing, andinstitutional accountability, so as to achieve a dynamicbalance between privacy protection and effective governance.1. IntroductionBetween the exponential growth of the global data volume and the release ofdata value lies a fundamental governance challenge: increasingly stringentrequirements for privacy and security protection mean that dispersed andheterogeneous parties cannot freely share data (Li Shuyuan et al., 2022), and massiveamounts of data remain scattered across mutually isolated institutions and systems,forming “data silos” that are difficult to overcome. The deep root of this dilemma liesin the non-rivalrous and easily replicable nature of data—centralized aggregationamplifies the risk of leakage and misuse, while isolation prevents the release of datavalue at the macro level. The classic proposition of data governance can therefore bestated as: how can the effective circulation and aggregation of data value be achievedwhile safeguarding data privacy and security?Privacy computing—particularly federated learning and secure multi-partycomputation—offers a breakthrough technical response to this proposition. McMahanet al. (2017) point out that modern mobile devices hold large amounts of data suitablefor training models that could greatly improve on-device user experience; however,this rich data is often privacy-sensitive, large in volume, or both, which can precludelogging it to a data center and training there using conventional approaches. Theytherefore advocate an alternative approach in which training data is distributed acrossmobile devices and a shared model is learned by aggregating locally computedupdates, terming this decentralized approach federated learning. As an emerging
84technology in the field of artificial intelligence, federated learning simultaneouslyaddresses the problems of “data silos” and privacy protection, uniting dispersed dataholders to train a global model while keeping each party’s data local (Xiao Xiong etal., 2023). By exchanging model parameters rather than raw data between clients andthe parameter server, federated learning effectively protects data privacy and security(Jiang Weijin et al., 2026). It thereby achieves data that is “usable but not visible”—atechnical route that precisely responds to the long-standing tension between dataprivacy protection and value release, and one that is widely regarded as the keyinfrastructure for breaking down data silos.However, any technical solution that resolves an existing problem also tends toendogenously generate new governance challenges. As data and computing powermove toward the network edge, “the development of artificial intelligenceapplications increasingly relies on privacy-sensitive user data,” and federated learning,owing to its emphasis on privacy protection, has gradually become a widely adopteddistributed machine-learning framework. At the same time, research on horizontalfederated learning spans multiple fields—machine learning, distributed systems,wireless communication, and information security—and exhibits diversity fromresearch motivation to technical approach (Wu Wentai et al., 2025). Under adistributed training architecture, the central aggregator cannot verify whether eachparticipant correctly generates local model updates according to the specifiedalgorithm. While this feature dissolves the privacy risks inherent in traditional datasharing, it brings an equally important problem to the surface: when auditors cannot“see” the raw data, how can the correctness of the computation process be confirmed?How can the authenticity of privacy-protection commitments be verified? How canaccountability be traced in multi-party collaboration?The effective operation of auditing, in institutional logic, depends on aself-evident premise—“visibility”: auditors must be able to access the behavioraltraces and evidentiary results of the audited party in order to make compliancejudgments. Privacy protection and transaction-data auditing are two relatively
85conflicting requirements among blockchain-system stakeholders (Gai Keke et al.,2025); verification requires transparent access, while privacy protection requiresinformation concealment—a tension that is especially pronounced inencrypted-computing environments. Under the privacy-computing framework, thisconflict is pushed to its extreme: data is encrypted and stored in a distributed manner,and the visibility premise of auditing is systematically dissolved at the technical level.Although combining verifiability with privacy-preserving methods offers clearbenefits, certain challenges remain before this combination can be widely adopted inpractice (Bontekoe et al., 2025). Although technical explorations combining the twohave emerged, a notable gap remains between technical feasibility and institutionalauditability. This forms the core paradox of data governance in the era of privacycomputing.Existing research on privacy computing has mostly concentrated on technicaldimensions such as algorithmic security proofs and efficiency optimization. As anemerging technology that builds machine-learning models using distributed trainingdatasets, federated learning “can effectively address the problem of local data privacyleakage caused by joint modeling among different data users”; however, “existingfederated learning systems have been shown to face potential threats at thedata-collection, training, and inference stages, endangering both data privacy andsystem robustness” (Gao Ying et al., 2023). Only a small number of studies havebegun to touch on the design of verifiable computation and audit mechanisms.Bontekoe et al. (2025) analyzed existing solutions that combine verifiability withprivacy-preserving computation on distributed data, classifying and comparing 41different schemes and discussing some of the most promising approaches in this area.In other words, the academic community has already undertaken preliminaryexploration of “how to technically achieve auditability,” but has yet to fully answer amore fundamental governance question: “why does auditing become so difficult underprivacy computing?”This paper aims to fill this research gap, with its core inquiry organized around
86three questions: how does the “trust black box” of audit accountability arise under theprivacy-computing framework? What are its deep roots? And how can governancereconstruction achieve a leap from “technical trust” to “institutional trust”?To address these questions, this paper draws on five complementary methods:normative research, model analysis, case analysis, comparative analysis, and literatureresearch. Normative research runs throughout the paper and is used to justify thelegitimacy of governance reconstruction; model analysis is used for the formalmodeling in Chapter 3; case analysis draws on two representative privacy-computingdeployment cases in finance and healthcare for in-depth examination; comparativeanalysis is used to reveal differences in audit visibility between centralized anddistributed architectures; and literature research provides support for the theoreticalfoundation. These methods correspond respectively to the three research stages of“diagnosis—attribution—prescription.”This paper unfolds a progressive analysis across three levels:First, by integrating information asymmetry theory with findings on verifiablecomputation, it constructs an analytical framework for the mechanism by which thetrust black box arises;Second, starting from the structural contradiction between the “invisibility” ofthe technical architecture and the “visibility” presumption of audit institutions, itexamines the formation logic of the trust black box and its institutional manifestationsacross three dimensions—traceability failure, compliance failure, and ambiguousaccountability;Finally, building on this analysis, it proposes a governance reconstructionscheme centered on the embedding of verifiable computation, independentalgorithmic auditing, and joint accountability determination. The concluding sectionsummarizes the findings and offers corresponding recommendations.2. Theoretical Foundation and Analytical Framework
87A fundamental challenge in data governance is the long-standing tensionbetween releasing the value of data and protecting privacy and security. Privacycomputing—represented by federated learning and secure multi-partycomputation—attempts to resolve this tension through the technical pathway ofmaking data “usable but not visible.” Yet its distributed, encrypted computingarchitecture, while cutting off traditional privacy risks, also systematicallyrestructures the informational foundation on which audit accountability depends. Thetheoretical question raised by this paradox is: when auditors cannot “see” the data,how can traditional visibility-based audit logic remain self-consistent? What blindspots exist in existing research’s response to this? What analytical tools does thispaper need to address this predicament? Around these questions, this chapter reviewsthe contributions and limitations of existing research along three dimensions—thetechnical logic of privacy computing, technical solutions for verifiability, and theinstitutional conditions of data circulation—before distilling the theoretical foundationand analytical framework of this paper.2.1 Information Asymmetry and Principal–Agent TheoryPrincipal–agent theory concerns how, under conditions of informationasymmetry, a principal can effectively supervise and incentivize an agent. Inprivacy-computing scenarios, the data provider (principal) “entrusts” data processingto a privacy-computing platform or aggregation server (agent), but because the rawdata is invisible, the principal cannot directly verify whether the agent’s behaviorconforms to the agreement, giving rise to typical adverse selection and moral hazard.Federated learning systems “have been shown to face potential threats at thedata-collection, training, and inference stages” (Gao Ying et al., 2023), and researchon horizontal federated learning spans multiple fields such as distributed systems andinformation security, exhibiting diversity from research motivation to technicalapproach (Wu Wentai et al., 2025). These characteristics further intensify, in adistributed environment, the difficulty principals face in monitoring agents’ behavior.Information asymmetry theory therefore provides a foundational analytical tool for
88understanding the erosion of trust in privacy computing.2.2 Verifiable Computation and Zero-Knowledge Proof TheoryVerifiable computation is a frontier branch of cryptography that studies how acomputing party can prove the correctness of a computation result to a verifying partywithout disclosing sensitive data. Bontekoe et al. (2025) trace the research progress ofintegrating verifiability into privacy-preserving computation. Zero-knowledge proof,as a core technique, allows one party to prove that a statement is true to another partywithout revealing any additional information. In the Masquerade scheme of Mourisand Tsoutsos (2024), a tailored multiplicative commitment scheme is proposed toensure the integrity of data aggregation, with all participants’ commitments publishedon a ledger to provide public verifiability. Zhang et al. (2025) propose a verifiablefederated learning system called Zkfhed, which designs a two-stage audit mechanismbased on zero-knowledge proofs to verify the provenance of training data and thecorrectness of the computation process; it also leverages fully homomorphicencryption for homomorphically encrypted delegated learning to achieve secureoutsourcing of computation tasks, effectively identifying malicious clients whileremaining efficient and scalable in terms of online time and communication overhead.This body of theory forms the technical foundation of this paper’sgovernance-reconstruction proposal—namely, that the core of auditing should shiftfrom “data visibility” to “process verifiability.”2.3 The Institutional Logic of Data Circulation and Governance ParadigmsFrom an institutional-design perspective, data circulation is essentially a form ofdata-use licensing. The value of data lies in cognizing the world; big data andartificial intelligence have transformed the means of acquiring and analyzing data.Data circulation is essentially a matter of data-use licensing, encompassing threemodes—one-to-one licensing, one-to-many licensing, and mutual licensing—thattogether constitute the pattern of socialized data utilization (Gao Fuping, 2019).However, the “invisibility” characteristic of privacy computing dissolves thisinstitutional premise at the technical level. Privacy computing harbors multiple risks,
89including data-compliance risk, data-leakage risk, discrimination-amplification risk,data-archipelago risk, and trust-disintegration risk, all of which urgently require legalregulation (Yin Huarong & Wang Huimin, 2022). Administrative-law regulation canachieve full-chain governance covering the ex-ante, interim, and ex-post stages,offering advantages of timeliness, flexibility, and systematicity (Yin Huarong &Wang Huimin, 2022). This structural contradiction constitutes the deep institutionalroot of what this paper terms the “trust black box.”To answer this question, this paper introduces information asymmetry andprincipal–agent theory as the structural analytical tool for understanding the erosionof trust in privacy computing, introduces verifiable computation theory as thetechnical foundation for an alternative audit paradigm, and takes the institutional logicof data circulation as the institutional design framework for governance reconstruction.These three correspond respectively to the progressively deeper analytical stages of“how the trust black box arises” (the principal–agent dilemma under informationasymmetry), “how it can be resolved” (the shift from data visibility to processverifiability), and “how it can be institutionalized” (third-party auditing and jointaccountability determination), forming the theoretical skeleton of“diagnosis—attribution—prescription” that runs through the paper.3. The Formation Logic and Core Dilemmas of the Trust Black Box3.1 Technical Root: The Paradigm Shift from “Visible” to “Invisible”Traditional centralized data governance follows the logic of “aggregate first,audit later”—once data is aggregated onto a centralized platform, auditors can directlyretrieve the raw data to complete their review, and the effectiveness of this processdepends on the “visibility” of data at both the physical and logical levels. Privacycomputing—especially as represented by federated learning—completely overturnsthis premise.This paper adopts the objective-function definition and notational system of theFederated Averaging algorithm (FedAvg) proposed by McMahan et al. (2017). In
90standard machine learning, the global empirical risk is defined as the average lossover all samples:minw∈ℝd f(w) where f(w) := (1/n) Σi=1n fi(w). (1)Since the data is partitioned across K clients, let the k-th client hold a local indexset of data Pk, with data volume nk = |Pk|, and let the total global data volume be n =Σk=1Knk. Equation (1) can then be equivalently rewritten as a weighted average ofeach client’s local loss function:f(w) = Σk=1K (nk/n)Fk(w) where Fk(w) = (1/nk) Σi∈Pkfi(w). (2)Equation (2) reveals the core architecture of federated learning: the optimizationobjective of the global model is decomposed into a weighted combination of multipleclients’ local objectives. In each round of communication, each client independentlycomputes updates on its local data Pk, and the server can only receive the local updateresults uploaded by each client (such as gradients or model parameters), while neverbeing able to directly access any raw data sample within Pk. Unlike centralizedstorage and processing by a data collector, federated learning distributes allparticipants’ data locally and conducts joint training; no participant may accessanother participant’s local data or other sensitive information without authorization,which effectively safeguards user privacy and data security (Xiao Xiong et al., 2023).Yet this causes the “visibility” premise of auditing to be systematicallydissolved—standing at the server side, the auditor can only access the aggregatedresult on the right-hand side of Equation (2), f(w) = Σ(nk/n)Fk(w), and cannotindependently verify whether the computation of any given Fk(w) is correct or strictlygenerated based on Pk.To quantify how this “invisibility” erodes audit effectiveness, define the auditvisibility index V as the proportion of the total information that the auditor candirectly obtain as effective information. In a centralized scenario, data is aggregatedon a single platform and the auditor can directly access the complete dataset, so V ≈ 1;whereas under the federated learning framework, letting the raw data space be X and
91the information space accessible to the auditor be A (such as the gradient ormodel-parameter space), since the mapping from raw data to uploaded information isa many-to-one, non-injective function, the auditor cannot uniquely infer the raw data.Hence:V = H(A)/H(X) ≪ 1. (3)Here H(·) denotes information entropy. Equation (3) shows that, based solely onthe uploaded aggregate information, the auditor cannot reconstruct or verify the truestate of the raw data—this protects privacy, but also means a substantiveimpoverishment of audit information. In federated learning, client-node data is usablebut not visible, and it is very difficult for the server node to audit the data quality andhistorical behavior of client nodes (Wang Chenyi, 2025). The technical root of thetrust black box lies precisely here—the paradigm shift from “visible data” to“invisible aggregated results” means that the informational foundation on whichauditing depends is actively curtailed by the algorithmic architecture itself.3.2 Institutional Dilemma: Three Manifestations of Audit Failure3.2.1 Dilemma One: Failure of Data TraceabilityThe primary step in data auditing is to trace the complete lifecycle of data—fromcollection and processing to every stage of computation, a verifiable “data lineage”must be established. However, in an encrypted computing environment, raw dataexists in encrypted form and circulates among multiple participants, renderingtraditional plaintext-identifier-based traceability techniques entirely ineffective. GaiKeke et al. (2025) note that privacy protection and transaction-data auditing are tworelatively conflicting requirements among blockchain-system stakeholders;verification requires transparent access while privacy protection requires informationconcealment, a tension especially pronounced in encrypted computation. Defining thetraceability reliability ρ as the probability that an auditor can correctly trace theraw-data source on which a given computational output depends, then in a scenariowhere K clients jointly train a model, this probability is no better than random
92guessing:ρ = Pr(successful tracing | output jointly generated by {Pk}k=1K) ≤ 1/K + ε. (4)Here ε is an arbitrarily small positive number (Lan & Zhang, 2025). Equation (4)shows that as the number of clients K increases, the probability of successful tracingapproaches zero—this is the intrinsic mathematical expression of the traceabilityfailure caused by “invisibility.”This failure is not merely theoretical speculation; it has already manifested as anobservable practical dilemma in real-world deployments. In 2026, JPMorgan’sKinexys blockchain business unit, together with BNY, RBC, DeepTempo, andNVIDIA, completed a proof-of-concept for federated-learning-based fraud detection,using NVIDIA FLARE as the federated-learning development kit and running tests ina multi-site federated environment provided by NVIDIA DGX Cloud. Its core designlogic is that each participating institution independently trains an AI model in its localenvironment, uploading only encrypted model updates and insights, while rawtransaction data always remains behind each institution’s own firewall. Theproof-of-concept results showed that the model trained via federated learningsignificantly outperformed models trained independently by a single institution infraud detection. However, when the anti-fraud model produces false positives or falsenegatives, auditors face precisely the dilemma described by Equation (4)—letting Kdenote the number of financial institutions participating in the anti-fraud training,each bank holding its own local transaction dataset Pk, the auditor, standing at thelevel of the central aggregator, can only access the encrypted model updates uploadedby each bank and cannot trace a specific prediction result back to which bank’sparticular historical transaction data it depended on. As the number of participatinginstitutions K increases, the probability of successful tracing ρ rapidly approacheszero, and the auditor essentially loses any reliable capacity to trace model outputs.Federated learning enables institutions to “collaboratively train AI models withoutsharing sensitive transaction data”—the flip side of this privacy-protection advantage
93is precisely that the traceability foundation of auditing is actively removed by thealgorithmic architecture itself. The localized execution of federated learning lacksthird-party visibility into the training process, which increases the necessity ofverifying that process (Ma et al., 2024).3.2.2 Dilemma Two: Failure of Compliance VerificationEnsuring the correctness and integrity of aggregated results is a basicprerequisite for privacy-computing systems to gain institutional trust, and ensuring theintegrity of aggregated results remains a key research focus for protecting users’legitimate interests (Li et al., 2025). Privacy-computing systems typically introducemathematical tools such as differential privacy (DP) to quantify the degree of privacyprotection. In practical federated learning, the objective function after incorporatingdifferential privacy is typically written as:minw f(w) + λ·Π(w).(5)Here f(w) is the global empirical loss defined in Equations (1) and (2), Π(w) isthe privacy-leakage risk function (such as an upper bound on the L2 sensitivity of thegradient, maxP,P′‖∇F(w;P) − ∇F(w;P′)‖2, or an L2 regularization term on modelparameters), and λ ∈ (0, ∞) is the trade-off coefficient. Equation (5) shows that themodel’s optimization objective shifts from “pursuing accuracy alone” to “acompromise between accuracy and privacy.” However, the setting of λ is essentially agovernance decision—too large and model utility is severely impaired, too small andprivacy protection becomes nominal. Currently, the vast majority ofprivacy-computing systems leave this parameter to be decided internally by thedevelopment team, lacking institutionalized audit oversight. The key to the failure ofcompliance verification is that, even when the mathematical proof of differentialprivacy is complete, errors in algorithmic implementation, programming bias, andeven the non-transparency of parameter selection can cause the actual level ofprotection to fall far short of the theoretical value. If auditors cannot access the basison which λ was set and its real impact on the final model, compliance assessment
94loses any operable foothold.The Health-FedNet privacy-preserving federated-learning framework proposedby Ali et al. (2025) provides a precise illustration of this failure. The framework isdesigned to allow multiple healthcare institutions to collaboratively trainchronic-disease prediction models without transmitting raw clinical information;Health-FedNet integrates differential privacy (DP), homomorphic encryption (HE),and an adaptive node-weighting mechanism to enhance privacy, scalability, androbustness against heterogeneous data distribution, and experiments on the MIMIC-IIIclinical database show that Health-FedNet improves diagnostic accuracy by 12%compared with a centralized model. However, an independent third-party auditorwould face precisely the fundamental obstacle revealed by Equation (5)—the value ofλ, the implementation details of the differential-privacy noise mechanism, and thespecific parameters of the secure aggregation protocol are all decided internally by thedevelopment team, and the auditor cannot independently verify the basis for thesesettings or their true impact on the final model. Even where the mathematical proof ofdifferential privacy is theoretically complete, programming bias in implementation,non-transparency in parameter selection, and even differences in data formats acrosshospitals could all cause the actual level of privacy protection to fall far short of thetheoretically claimed level. In the Health-FedNet case, auditors are essentiallyexcluded from the key decision-making stages of compliance verification—this is atypical manifestation of the failure of compliance verification.3.2.3 Dilemma Three: Ambiguous AccountabilityUnder a distributed computing framework, when the final model exhibits bias,discrimination, or data leakage, should responsibility fall on the data provider, thealgorithm designer, or the platform operator? The traditional logic of “whoeverproduces is responsible” fails entirely in multi-party privacy-computing scenarios.Horizontal federated learning spans multiple academic fields (Wu Wentai et al., 2025),and the complexity of the responsibility chain grows exponentially. In an encryptedenvironment, auditors cannot obtain the raw data or complete algorithmic logs of each
95participant, so the attribution of responsibility can only rest on ambiguous inference.The two basic federated-learning deployment cases discussed above jointlyreveal the real-world root of ambiguous accountability under multi-partycollaboration. In the Kinexys financial project, which was not equipped withaudit-enhancing modules such as verifiable traceability or on-chain evidencerecording, the failure of traceability meant that auditors could not determine whichparticipant’s local training bias caused a given anti-fraud false positive, andfine-grained accountability determination therefore lacked a reliable datafoundation—was it the data provider introducing systematic bias during local labeling,a flaw in the algorithm designer’s aggregation strategy, or improper parameterconfiguration by the platform operator? Auditors could not make a precise, actionabletraceability judgment on this. In the Health-FedNet healthcare scenario, built on acomparable underlying architecture, insufficient compliance-audit capability meantthat auditors could not fully verify whether the final model truly honored itsprivacy-protection commitments (Ali et al., 2025); once a membership-inferenceattack or data-leakage risk occurs, responsibility—whether it lies with thedevelopment team (improper choice of privacy parameters), the medical institution(non-compliant local data handling), or the framework designer (vulnerabilities in theunderlying security protocol)—likewise falls into an ambiguous state with noevidence to support fine-grained accountability. What is particularly concerning isthat this ambiguity of rights and responsibilities arising from the absence offine-grained traceability may be strategically exploited by participants—“thedecentralization of responsibility evolves, in substance, into responsibility that isnowhere to be found,” as all parties can shift blame behind the algorithmic black boxof encrypted computation, further reducing their willingness to actively cooperatewith in-depth auditing.3.3 The Formation Mechanism of the Trust Black Box: A Self-ReinforcingTriple-Dilemma CycleThe three dilemmas above do not exist independently but instead form a
96self-reinforcing vicious cycle: the failure of traceability strips compliance verificationof its data foundation; the failure of compliance verification leaves responsibilitydetermination without any basis; and ambiguous accountability reduces participants’willingness to cooperate with audits, further aggravating the difficulty of traceability.These three links form a positive-feedback loop, which can be approximatelydescribed using a system of difference equations:ρt+1 = ρt − μ1(1 − Ct),Ct+1 = Ct − μ2(1 − ρt) − μ3ψt,ψt+1 = ψt + μ4(1 − Ct). (6)Here ρt is the traceability reliability in audit cycle t, Ct is the effectiveness ofcompliance verification, ψt is the uncertainty of responsibility attribution, and μ1, μ2,μ3, μ4 > 0 are feedback-intensity coefficients. Equation (6) shows that, absentexternal intervention, the system will automatically tend toward the extreme states ofρ → 0, C → 0, and ψ → ∞—that is, the total collapse of the audit system. As thiscycle approaches its critical point, privacy computing degenerates from a “trustsolution” into a “trust black box”—technically, data circulation remains achievable,yet data subjects find it difficult to understand how privacy computing processes andoperates their data, casting doubt on the accuracy and reliability of the output results(Lu Anwen, 2025), while at the institutional level no effective trust in the circulationprocess can be established at all.
97Figure 1: Positive feedback causal loop of the privacy computing audit dilemmaIn summary, the trust black box is not merely a technical flaw, but a systemicgovernance crisis arising from a mismatch between the technical architecture andinstitutional arrangements. Its formation logic can be summarized as follows: privacycomputing protects privacy through “invisibility,” but in doing so unexpectedly seversthe “visibility” information chain on which auditing depends, thereby triggering acascading reaction across the three failures of traceability, compliance, andaccountability, and ultimately generating, at the institutional level, an impenetrableblack box. This understanding lays the problem foundation for thegovernance-reconstruction proposal that follows.4. Governance Reconstruction: From Technical Trust to InstitutionalTrustThe analysis above shows that the trust black box arises from the structuralrupture between the “invisible” architecture of privacy computing and the “visibility”premise of auditing, and that its triple dilemma continuously reinforces itself througha positive-feedback loop, ultimately driving the audit system toward collapse. Thisraises the core question: can this governance crisis be effectively resolved? Thischapter argues that the key to resolving the trust black box does not lie in abandoningthe technical route of privacy computing, but in driving a fundamental transformationof the audit paradigm from “based on data visibility” to “based on processverifiability.” This transformation requires simultaneous progress along threedimensions: technical embedding, institutional design, and regulatory coordination.4.1 Audit Paradigm Transformation: From “Data Visibility” to “ProcessVerifiability”The essence of the trust black box is that “what cannot be seen cannot be audited.”The logical starting point for resolving it is therefore that auditing need not require“making data visible,” but should instead move toward “processverifiability”—achieving independent verification of the correctness, integrity, and
98compliance of the computation process without sacrificing privacy.This paradigm shift already has feasible technical support. Bontekoe et al. (2025)systematically review research progress on combining verifiability withprivacy-preserving computation in order to preserve confidentiality while maintainingcorrectness; applications that are data-intensive and require strong privacy protectionespecially need verifiable correctness guarantees in outsourcing scenarios. TheMasquerade scheme of Mouris and Tsoutsos designs a multiplicative commitmentmechanism that ensures the integrity of multi-party data aggregation and publishes allparticipants’ commitments on a ledger to provide public verifiability. The Zkfhedsystem proposed by Zhang et al. (2025) goes further, designing a two-stage auditmechanism based on zero-knowledge proofs that can simultaneously verify theprovenance of training data and the correctness of the computation process. Inaddition, the development of decentralized trusted federated-learning aggregationschemes that incorporate zero-knowledge proofs, together with verifiable confidentialcloud-computing protocols, further demonstrates that “privacy” and “auditability” arenot irreconcilable contradictions but can, at the technical level, coexist synergistically.However, technical solutions only provide the possibility of transformation;without corresponding institutional arrangements, technical capability will struggle totranslate into actual audit effectiveness.4.2 Institutional Reconstruction: Third-Party Auditing and AccountabilityMechanismsAt the institutional level, the transformation of the audit paradigm requires theconstruction of three core mechanisms.4.2.1 Independent Third-Party Algorithmic AuditingThe algorithmic design, parameter settings, and computation processes ofprivacy-computing platforms should be subject to periodic auditing by third-partyinstitutions independent of the developers and operators. The independence ofthird-party auditors can effectively alleviate the structural difficulty, inherent in the
99principal–agent framework, of monitoring agent behavior—when the principal cannotdirectly verify whether the agent’s behavior conforms to the agreement, anindependent third-party review mechanism is the only feasible institutional alternative.Existing research has proposed a lightweight, blockchain-based privacy-preservingaudit framework that builds a secure and efficient computing model, supporting dataintegrity, traceability, and confidentiality in practical application scenarios such asmedical-record systems and financial audit platforms.4.2.2 Joint Accountability Determination MechanismThe traditional one-way logic of “whoever produces is responsible” fails entirelyin multi-party privacy-computing scenarios. Responsibility should be extended fromthe single entity of the “data provider” to a shared, multi-party arrangement,establishing a joint-responsibility framework based on “algorithmic contribution plusdegree of fault.” Zhu Jianming et al. (2021) note that federated learning is adistributed machine-learning framework that stores data locally at participating nodesand can effectively protect the data privacy of intelligent edge nodes; existingfederated-learning approaches typically upload intermediate model-trainingparameters to a parameter server for model aggregation, a process with two problems:first, the privacy leakage of intermediate parameters—existing privacy-protectionschemes typically add noise to intermediate parameters via differential privacy, butexcessive noise degrades the quality of the aggregated model; second, theself-interested and fully autonomous nature of node training may lead maliciousnodes to upload false parameters or low-quality models, affecting the aggregationprocess and model quality (Zhu Jianming et al., 2021). To address these dilemmas,that study reconstructs the centralized parameter server of federated learning into adecentralized parameter-aggregation chain, using blockchain to record theintermediate parameters of the model-training process as evidence, and incentivizingcollaborating nodes to verify model parameters while penalizing participants whoupload false parameters or low-quality models, thereby constraining theirself-interested behavior. At the same time, it uses model quality as an evaluation basis
100to achieve dynamic adjustment of the privacy noise applied to intermediateparameters and adaptive model aggregation. Its prototype experiments confirm thatthe model not only enhances mutual trust among federated-learning participants butalso prevents the leakage of intermediate-parameter privacy, thereby achieving atrusted federated-learning model with enhanced privacy protection. Thisblockchain-based auditable framework, through a consensus algorithm based on proofof node contribution and an asynchronous parameter-audit mechanism, provides asolid technical reference for the quantified apportionment of joint responsibility.Specifically, when the final model exhibits bias, discrimination, or data leakage,responsibility should be apportioned proportionally according to each party’s actualcontribution to and degree of fault in model generation—contribution can bemeasured by the information content of gradient updates or the sensitivity of modelparameters, while the degree of fault must be determined in conjunction with specificviolations identified through third-party auditing.4.2.3 Systematic Improvement of Laws and RegulationsThe legal limitations of privacy protection in federated learning (Liu Zegang,2025) reflect the fact that regulatory development lags behind technological progress.Coordinated progress on five measures—improving laws and regulations, buildingunified standards, consolidating the trust system, strengthening regulatoryenforcement, and fostering compliance awareness—should be pursued to provide aclear compliance framework and institutional safeguard for the development ofprivacy computing.4.3 Regulatory Innovation: Standardization and Cross-Domain CollaborationThe effective implementation of institutional reconstruction cannot be achievedwithout standardization at the regulatory level and international coordination.At the domestic level, dedicated standards for privacy-computing audits shouldbe developed. Existing research has proposed an integrated protocol combiningverifiable confidential cloud computing with data-integrity auditing across both
101cloud-storage and cloud-computing scenarios, achieving coordinated functionalityacross three tasks under ciphertext: verification of cloud-computing results, integrityauditing of cloud-stored data, and privacy protection of cloud-stored data. Building onsuch technical explorations, dedicated audit standards should be further developed toclarify the qualifications of audit subjects, the scope of audit objects, the requiredfrequency of audits, and the norms for audit methodology, so that third-party auditinghas a clear and standardized basis to follow.At the international level, data circulation supported by privacy computing isinherently cross-border in nature, requiring active international cooperation toestablish common rules for cross-border privacy-computing audits, avoiding new“compliance silos” caused by fragmented regulatory standards across countries.To comprehensively assess the implementation effects of thegovernance-reconstruction scheme above, a data-circulation governance index G canbe constructed to quantitatively measure the governance level of a privacy-computingsystem. It is defined as:G = α·A + β·T + γ·E. (7)Figure 2. Synergized governance architecture for standardization andcross-domain collaboration.
102Here A is auditability, measuring the system’s capacity for independentverification of the computation process; T is trustworthiness, reflecting participants’confidence that the system operates as expected; E is efficiency, a composite indicatorencompassing computational efficiency, communication overhead, and audit cost; andα, β, γ ∈ (0,1) are the weighting coefficients for each dimension, satisfying α + β + γ= 1. The construction logic of this index echoes the dynamic-systems analysis shownin Equation (6)—auditability A is the core lever variable for breaking thepositive-feedback loop of the triple dilemma. The core objective of governancereconstruction is to raise A from its currently low level (estimated at roughly 0.2–0.3)to above 0.8, moving G into the high-governance-effectiveness range (≥ 0.7). Thisquantitative framework offers a practicable, unified tool for evaluating and comparingthe governance levels of different privacy-computing systems.Equation (7) sets auditability A as the core explanatory variable of thegovernance index G. Drawing on discussions of threshold effects in the field oftechnology governance, improvements in governance effectiveness typically displayS-shaped growth—initially constrained by insufficient coupling between institutionsand technology and thus growing slowly, then entering a phase of rapid release aftercrossing a critical threshold, and finally tending toward saturation. Based on thistheoretical expectation, this paper uses a logistic growth model to numericallysimulate how G changes with A, with the functional form:G(A) = Gmax / (1 + e−k(A−A0)) (8)Here Gmax is the theoretical upper bound of the governance index, k is thegrowth-rate coefficient, and A0 is the inflection point. Parameter values are calibratedaccording to the marginal contribution rate of the αA term in Equation (7), holding Tand E at their baseline levels so as to isolate the net effect of A on G. Figure 3presents the numerical simulation results under this model.
103Figure 3. The impact of auditability (A) on the data circulation governance index(G).Note: The curve is derived from the logistic growth model (Equation (8)), withparameters calibrated according to the marginal contribution rate of the αA term inEquation (7); the red dot marks the current-state anchor (A ≈ 0.25, G ≈ 0.30),corresponding to the “locked-in state” described in Section 3.3; the green dot marksthe governance-reconstruction target anchor (A = 0.80, G ≈ 0.72), corresponding tothe expected effects of the three institutional innovations in Section 4.2; the greydashed lines mark the threshold (A ≥ 0.7, G ≥ 0.7). This figure is a theoreticalsimulation intended to reveal the threshold effect of A as the core lever variable,rather than an empirical measurement.Figure 3 intuitively illustrates the above simulation results: when auditability Ais below 0.3, the governance index G is locked into a low-effectiveness range below0.4—precisely the “low-level equilibrium” state corresponding to the self-reinforcingtriple-dilemma cycle described in Section 3.3; whereas once A crosses the 0.7threshold, G rapidly rises into the high-effectiveness range above 0.7. The gap
104between the current-state point (A ≈ 0.25, G ≈ 0.30) and the target point (A = 0.80, G≈ 0.72) shows that marginal improvement alone cannot break the governance impasse;a structural leap must be achieved through the three institutional innovations proposedin Section 4.2—the embedding of verifiable computation, independent third-partyalgorithmic auditing, and joint accountability determination. This quantitative analysisprovides a clear institutional-effectiveness target for this paper’sgovernance-reconstruction proposal and lays an operable evaluative benchmark forthe policy recommendations that follow.5. Conclusion and RecommendationsPrivacy computing offers a revolutionary pathway for breaking data silos andreleasing the value of data elements. However, while “usable but not visible”dissolves privacy risks, it also creates a trust black box in which “what cannot be seencannot be audited.” This paper has revealed the formation logic of this trust blackbox—the structural contradiction between privacy protection and audit visibility—andhas shown that the “black-box problem,” whereby the central aggregator in federatedlearning’s distributed architecture cannot verify client model updates, is a typicaltechnical manifestation of this contradiction. On this basis, the paper hasdemonstrated, across the three dimensions of traceability failure, compliance failure,and ambiguous accountability, the self-reinforcing cycle underlying the auditdilemma.The findings suggest that the key to resolving the trust black box does not lie inabandoning privacy computing, but in driving a fundamental transformation of theaudit paradigm from “based on data visibility” to “based on process verifiability.” Thematuration of technologies such as zero-knowledge proofs and verifiable computationprovides feasible support for this transformation, while institutionalized third-partyauditing and accountability mechanisms are the necessary safeguard for putting it intopractice.Accordingly, this paper offers the following recommendations:
1051. Technical standards first: incorporate auditability as a core design principleof privacy-computing systems, mandating the configuration of verifiable-computationmodules and audit interfaces;2. Establishment of institutional bodies: set up independent algorithmic-auditseats within data exchanges, and establish third-party auditing andjoint-accountability-determination mechanisms;3. Refinement of regulatory rules: formulate dedicated standards forprivacy-computing audits, and promote compliance certification of auditability andcross-domain coordination.Ultimately, audit-based trust in data circulation is not merely a technical or legalissue, but a fundamental institutional question bearing on whether the data-elementmarket can develop in a healthy manner. Only through the coordinated reconstructionof technology and institutions—namely, the organic unification of embeddingverifiable computation, third-party algorithmic auditing, and institutionalaccountability—can the benefits of privacy computing be enjoyed while safeguardingthe trust foundation of data governance.ReferencesLi, S., Ji, Y., Shi, D., Liao, W., Zhang, L., Tong, Y., & Xu, K. (2022). A datafederation system for multi-party security. Journal of Software, 33(3), 1111–1127.https://doi.org/10.13328/j.cnki.jos.006458.McMahan, B., Moore, E., Ramage, D., Hampson, S., & Agüera y Arcas, B.(2017). Communication-efficient learning of deep networks from decentralized data.In Proceedings of the 20th International Conference on Artificial Intelligence andStatistics (AISTATS) (Vol. 54, pp. 1273–1282). PMLR.DOI:10.48550/arXiv.1602.05629.Xiao, X., Tang, Z., Xiao, B., & Li, K. (2023). A survey of privacy protection andsecurity defense research in federated learning. Chinese Journal of Computers, 46(5),
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108From Tool to Order: The Dual Reshaping ofEmployment Structure and Human Social Structure byArtificial IntelligenceRao JiafenMajor of Big Data Management and Application, School of Business, NanfangCollege Guangzhou, 510970, ChinaAbstract: Artificial intelligence is penetrating every corner of the economy andsociety at a pace far exceeding expectations, and its impact on human society hasrisen from that of a mere technical tool to a structural reordering of society itself. Thispaper systematically analyzes the profound changes brought about by artificialintelligence along two dimensions: the employment structure and the human socialstructure. At the level of employment structure, artificial intelligence drives the labormarket from "polarization" toward "upgrading" through threemechanisms—substitution, creation, and industrial-structure transformation—givingrise to new structural contradictions such as "middle-tier collapse" and "career-ladderfracture." At the level of human social structure, artificial intelligence is reshaping themodes of knowledge production and cognition, intensifying the concentration ofwealth and power, restructuring the social division of labor and human-machinerelations, and giving rise to a new form of inequality—the "intelligence divide." Thestudy finds that the transformation of the employment structure and that of the humansocial structure are not isolated from one another, but are deeply coupled through atransmission chain running from "skills" to "income" to "power." In response to thisdual structural shock, this paper proposes a systematic response framework builtalong three dimensions: the restructuring of the education system, the transformationof the social security system, and the innovation of the governance paradigm.Keywords: Artificial Intelligence; Employment Structure; Social Structure; SkillSubstitution; Social Inequality; Intelligence Divide
1091. IntroductionIn 2025, the most striking development in the field of artificial intelligence wasnot a breakthrough in any single technology, but an increasingly clear consensus: AIis no longer a passive tool, but an active force reshaping social order. In the threeyears since ChatGPT ignited the wave of generative AI, large language models, AIagents, and embodied intelligence have iterated at an accelerating pace, and artificialintelligence is leaping from a specialized tool to a foundational structure of the socialsystem. China's 2026 Government Work Report proposed "building a new form ofintelligent economy" and explicitly called for "improving measures that adapt to thedevelopment of AI technology and promote employment and entrepreneurship,"marking the rise of AI's impact on the labor market from a technical issue to a matterof national strategy.Yet when people speak of AI "replacing jobs," they often see only the tip of theiceberg. The impact of artificial intelligence on human society goes far beyond"whether a particular position disappears"—it is changing the overall structure of thelabor market, reshaping the form of the career ladder, and redefining the relationshipbetween "work" and the "worker"; at the same time, at a deeper level, it is alsorestructuring society's modes of knowledge production, the logic of wealthdistribution, and patterns of interpersonal connection. As some scholars have pointedout, the disruptiveness of artificial intelligence manifests economically as creativedestruction, carrying a double-edged effect that markedly raises productivity whilealso disrupting employment and widening income inequality.This paper attempts to answer a central question: what picture emerges fromartificial intelligence's reshaping of the employment structure and the human socialstructure? What is the intrinsic relationship between these two structuraltransformations? And how should society respond to this dual shock? The study firstanalyzes the mechanisms and manifestations through which AI reshapes theemployment structure, then explores its deeper impact on human social structure, and
110finally reveals the coupling logic between the two structural transformations andexplores paths for governance.2. Literature Review2.1 Research Progress on the Impact of AI on EmploymentThe academic community has accumulated a relatively rich body of research onthe relationship between artificial intelligence and employment. Existing studiesmainly focus on AI's impact on the total volume of employment, on the employmentstructure, and on labor mobility and wages. At the level of the underlying mechanisms,scholars generally agree that AI affects the labor market through two pathways:substitution and creation. Under the substitution effect, intelligent systems aregradually replacing positions such as manufacturing assembly-line work andrepetitive basic white-collar tasks; the creation effect, meanwhile, has given rise toemerging occupations such as AI engineers and data analysts.Regarding trends in the employment structure, empirical results show that theapplication of robots has produced a certain substitution effect on firms' labor demand:for every 1% increase in industrial-robot penetration, firms' labor demand falls by0.18%; moreover, the effect of robot adoption differs significantly across labor ofdifferent skill levels, displaying a feature of "employment polarization," while robotadoption has no clear effect on firms' wage levels.1 The impact of AI on theemployment structure is a process evolving from "polarization" to "upgrading"—inthe early stage it manifests as a decline in mid-skill employment alongside growth inboth high- and low-skill employment, while in the later stage it shows an overall risein skill levels. Other scholars, drawing on skill-biased technological change theory,note that artificial intelligence produces a marked polarizing effect on employment.2.2 Research Progress on AI and Social StructureCompared with research on employment impacts, the systematic analysis of AI's1[1] Wang Yongqin & Dong Wen. (2020). How does the rise of robots affect China's labor market?— Evidence from listed manufacturing firms. Economic Research Journal, 55(10), 159-175.
111effect on social structure is still in its infancy. Existing research unfolds mainly alongthree dimensions: the first is inequality, focusing on how AI widens income gaps andpolarizes wealth; the negative shock of AI technology on occupational income is morelikely to cause unemployment and income loss among workers inlow-technical-complexity occupations, while it actually raises the income of those inhigh-technical-complexity occupations. It is thus evident that AI's impact onoccupations is differentiated, widening the income gap between high- andlow-technical-complexity occupations and giving rise to occupational inequality.2The second dimension is power, examining the structural imbalance of power broughtabout by data monopolies and the concentration of computing resources; the third isthe human-machine relationship, studying AI's impact on human subjectivity and therestructuring of social relations.Some research points out that one of the challenges brought by AI is a"de-intellectualizing" function, giving rise to a phenomenon of "artificial intellectualdisability," whereby the misuse of AI tools causes humans to inflict intellectual harmupon themselves, thereby profoundly affecting the social order. Put simply, "artificialintellectual disability" refers to an increasingly common phenomenon in the age of AI:self-inflicted intellectual harm resulting from the extensive and unrestraineduse—whether active or passive—of AI-related tools, especially internet-based socialmedia. It must be emphasized that this is not what is commonly referred to as"intellectual disability" in the everyday sense, which is often caused by organicdamage to the brain or by incomplete brain development leading to persistentimpairments in cognition and psychology—conditions that are congenital andunavoidable, and toward which people extend sympathy while continually seekingtreatments. "Intellectual impairment" in this new sense, however, is man-made: it canstem from the excessive or improper use of tools, or from the effects that the tools2[2] Wang Linhui, Qian Yuanyuan, Zhou Huilin & Dong Zhiqing. (2023). The impact of AItechnology shocks and the direction of occupational change in China. Management World, 39(11),74-95. https://doi.org/10.19744/j.cnki.11-1235/f.2023.0131
112humans have created exert on humanity itself.3 At the governance level, scholars callfor social governance systems to urgently shift from "passive response" to "proactiveadaptation."2.3 Research ReviewExisting research has made important progress in its respective fields, but two cleargaps remain. First, research on employment structure and research on social structureremain relatively disconnected, lacking a systematic analysis of their intrinsicrelationship. Second, most studies emphasize describing the problem and explainingits mechanisms, without delving deeply into the coupling logic between the twostructural transformations or into comprehensive governance pathways. Building onthis foundation, this paper attempts to construct an integrated analytical framework.3. Research MethodsThis study draws jointly on literature analysis and theoretical analysis.With respect to literature analysis, this paper systematically reviews academicpapers, policy documents, and industry reports in the fields of AI, employmentstructure, and social structure. Sources mainly include journal articles anddissertations indexed in the China National Knowledge Infrastructure (CNKI), policydocuments issued by national ministries, and research reports published byinternational organizations such as the World Economic Forum.With respect to theoretical analysis, this paper draws on skill-biasedtechnological change theory, task-biased technological change theory, and theories ofsocial structural change, analyzing AI's dual impact on the labor market and onsociety from a structural perspective. The analytical approach emphasizes logicalinference regarding mechanisms of change, evolutionary trends, and intrinsicconnections, rather than mere description of phenomena.4. AI's Reshaping of the Employment Structure3[3] Zheng Yongnian. (2025). Social order in the age of artificial intelligence. Bulletin of ChineseAcademy of Sciences, 40(4), 633-641. https://doi.org/10.16418/j.issn.1000-3045.20250318003
1134.1 A Triple Mechanism: Substitution, Creation, and Industrial-StructureTransformationDuring the expansion phase of technological progress, the scale of coreindustries expands rapidly, and the diffusion effect on related industries graduallyemerges, creating large numbers of jobs while also producing marked changes in theemployment structure.4 AI's impact on the employment structure is not asingle-dimensional matter of "replacement," but operates simultaneously throughthree mechanisms, as shown in Figure 1.Figure 1 AI's Impact on the Employment StructureThe substitution mechanism is the most direct and most anxiety-inducingpathway of impact. With its capacity for content generation and logical reasoning,generative AI has begun to systematically penetrate task domains such as translation,programming, and copywriting. Mid-skill positions with a high degree ofroutinization, standardization, and codification are becoming the core zone of impact.Substitution is not evenly distributed, however—simple repetitive manual labor andbasic mental labor face the risk of devaluation, while work requiring high originality,contextual judgment, and emotional interaction remains relatively safe.The creation mechanism offers another possibility. The development of AI hasgiven rise to numerous emerging industries and occupations—demand for positionssuch as algorithm engineers, data analysts, AI algorithm optimization specialists, and4[4] Wang Jun, Zhang Yuzhe, Zhang Yibo & Hong Qunlian. (2017). The mechanisms andcountermeasures by which AI and other new technologies affect employment. Macroeconomics,(10), 169-181. https://doi.org/10.16304/j.cnki.11-3952/f.2017.10.019
114AI data-annotation analysts has surged. The World Economic Forum's Future of JobsReport 2025 forecasts that by 2030, AI and data-processing technologies will create11 million jobs. However, the creation effect does not automatically offset thesubstitution effect—one cannot simply assume, based on historical experience, thatnew technology will create an equal or greater number of new jobs while eliminatingold ones.The mechanism induced by industrial-structure transformation runs deeper still.Artificial intelligence is driving the reallocation of labor across the primary,secondary, and tertiary sectors, shifting it toward the digital economy, modernservices, and related fields. Employment in China's traditional manufacturing sectorhas generally contracted, with services becoming the main source of employment. Allthree sectors now display a pattern of jobless economic growth—economic growth nolonger automatically translates into employment growth, a trend that warrants seriousvigilance.4.2 From "Polarization" to "Upgrading": The Evolutionary Trajectory of theEmployment StructureAI's impact on the employment structure is not static, but displays a clear stagedcharacter, as shown in Figure 2.
115Figure 2 Evolutionary Trajectory of the Employment StructureThe first stage (roughly 2009-2013) is the "polarization" stage. AI causedmid-skill employment to decline while high- and low-skill employment both rose.Mid-skill positions—those involving routine tasks performed according to explicitrules—were the first to be affected. This stage is typically characterized by the labormarket being squeezed toward both ends: the high end requiring greater cognitiveability, the low end requiring greater physical dexterity, while the "routine cognitive"work in the middle gradually shrinks.The second stage is the "upgrading" stage. As AI technology deepens, theemployment structure as a whole moves toward higher skill levels. The surge indemand for high-skill labor stands in sharp contrast with the pressure on low- andmid-skill workers to retrain, highlighting the structural contradiction within the labormarket. Data show that by 2025, more than nine in ten workers already treated AItools as a standard part of their jobs, and 19.18% of newly posted positions explicitlyrequired AI-related skills. Demand for algorithm-engineer positions grew 110.1%year on year—both an opportunity and a threshold.4.3 "Middle-Tier Collapse" and "Career-Ladder Fracture": New Structural RisksIf the analysis above sketches the macro picture of change in the employmentstructure, the latest trends of 2025-2026 reveal a more severe structural risk, as shownin Figure 3.
116Figure 3 Career-Ladder Fracture and "Middle-Tier Collapse""Middle-tier collapse" is one of the most prominent phenomena. Globally, adivergent pattern has emerged in which junior and mid-level white-collar positionsface marked pressure while high-end professional and leadership talent remainrelatively stable. In the Chinese market, demand has fallen for positions in sales,business development, branding, legal affairs, and some junior internet-technologyroles, a phenomenon rooted fundamentally in AI tools' efficiency-driven substitutionand structural restructuring of basic labor. The "hollowing-out" of mid-skill positionsis changing the shape of the entire career ladder."Career-ladder fracture" is another major concern. Firms tend to first scale backrecruitment and reduce entry-level positions—the narrowing of career entry pointsprecedes large-scale layoffs. This means the channel through which young peopleenter the workforce is narrowing, and the traditional career path of "starting at thebottom and gradually being promoted" may be cut off. When entry-level jobs arereplaced or reduced by AI, how the new generation of workers is to accumulate workexperience and advance their skills becomes an unresolved question.In addition, structural unemployment, pressure to upskill, and a widening
117income-distribution gap are compounding one another. In 2025, China's youthunemployment rate reached as high as 18.9%, and the flexibly employed populationexceeded 200 million. The rapid development of AI and the structural difficulties inthe labor market are reinforcing one another.5. AI's Deep Impact on Human Social StructureWe might borrow a concept from British writer Aldous Huxley's Brave NewWorld to describe the kind of uncertain social order toward which artificialintelligence may lead.5 If the change in employment structure represents the"surface" of AI's impact on society, then the deep transformation of human socialstructure is the "substratum."5.1 Restructuring of Knowledge Production and Cognitive ModesIn recent years, scholars have reached a consensus: although AI systems displayintelligent-seeming behavior across many tasks, they do not understand data the wayhumans do.6 The integration of algorithms into social interaction is changingtraditional modes of knowledge production and dissemination. As people obtain thelatest information through search engines and recommendation systems, and as theyobtain knowledge and guidance through ChatGPT, the intervention of algorithms notonly lowers the threshold for accessing knowledge but also promotes the formation ofself-organizing modes of knowledge collaboration.7Even more worthy of vigilance, generative AI displays a "structural positionalbias" on controversial issues—generating content whose persuasive force differsmarkedly for different groups or positions; in meeting personalized needs, algorithms5[5] Zheng Yongnian. (2025). Social order in the age of artificial intelligence. Bulletin of ChineseAcademy of Sciences, 40(4), 633-641. https://doi.org/10.16418/j.issn.1000-3045.202503180036[6] Mitchell, M., & Krakauer, D. C. (2023). The debate over understanding in AI's large languagemodels. Proceedings of the National Academy of Sciences, 120(13), e2215907120.https://doi.org/10.1073/pnas.22159071207[7] Halonen, N., Ståhle, P., Juuti, K., Paavola, S., & Lonka, K. (2023). Catalyst for co-construction:The role of AI-directed speech recognition technology in the self-organization of knowledge.Frontiers in Education, 8, 1232423. https://doi.org/10.3389/feduc.2023.1232423
118also inevitably give rise to problems of information manipulation and socialinequality,8 and may potentially participate in shaping public attitudes andconstructing social consensus, exacerbating social risks such as the digital divide andgroup polarization. This means that AI is not only a "producer" of knowledge, butpotentially also a "shaper" of social cognition—an influence that extends far beyondthe technical level.5.2 Concentration of Wealth and PowerThe rapid development of AI technology has markedly raised the efficiency ofthe economic system, but the sustained concentration of technological dividendsamong different groups is intensifying economic inequality along multipledimensions.At the level of wealth, the gains from AI are highly concentrated among a smallnumber of tech giants and highly skilled talent. Data monopolies and theconcentration of computing resources constitute a new type of power structure. Thedevelopment of AI has made capital relatively more important than labor in theproduction process, meaning that the distribution of national income tilts furthertoward capital and away from labor.9 Those who are able to master AI technologyand who possess data resources and computing power occupy an increasinglyadvantageous position in the distribution of wealth, while those left behind bytechnological change face falling incomes or even exclusion from the new economicsystem.At the level of power, technical rules exert a concealed governing functionthrough "black-box" operations, dominating ethical judgment and decision logic. Thedecision-making power of algorithms is expanding ever further—from credit approvalto hiring screening, from judicial sentencing to medical diagnosis, AI systems are8[8] Kalpokas, I. (2019). Affective encounters of the algorithmic kind: Post-truth and posthumanpleasure. Social Media + Society, 5(2). https://doi.org/10.1177/20563051198456789[9] Chen Yanbin, Lin Chen & Chen Xiaoliang. (2019). Artificial intelligence, aging, and economicgrowth. Economic Research Journal, 54(7), 47-63.
119taking over an increasing share of highly consequential human judgments. Whoevercontrols the algorithm controls the power to define the rules.5.3 Restructuring of the Social Division of Labor and Human-Machine RelationsArtificial intelligence is fundamentally changing the meaning of "work" and theidentity of the "worker."At the level of the division of labor, the evolution of production tools hasprofoundly shaped changes in the system of labor division. Machines, as an extensionof and substitute for physical labor, drove the formation of a physical division of labor;specialized AI, as an extension of and reinforcement for mental labor, is now drivingthe restructuring of a mental division of labor. When AI can replace not only physicallabor but also a substantial share of mental labor, humanity's place within thedivision-of-labor system must be redefined. As some scholars have argued, theultimate transformation of the AI era is not that machines replace human labor, butthat human beings and machines achieve a precise division of labor, allowing humanvalue to return—freeing humanity entirely from the bonds of mechanical, repetitivelabor and releasing the uniquely human values of creativity, empathy, and criticalthought.At the level of relationships, the human-machine relationship is undergoing ashift from "tool use" to "co-existence and collaboration." As algorithms increasinglypermeate the daily lives of ordinary people, human-machine interaction has becomean important medium for online interaction and virtual socializing, in turn connectingpeople's diverse and personalized needs and becoming embedded in the process ofshaping shared social values.10 As more and more people confide in AI and formemotional bonds with it, human social connection and cognitive patterns areundergoing a profound shock. The intelligent qualities that AI displays—qualities thattranscend biological limits—pose a challenge to the very essence of humansubjectivity. Preserving human subjectivity has become key to the sustainable10[10] Zhang Yue. (2024). AI algorithms reshaping social structure: Challenges and insights.Philosophical Analysis, 15(4), 32-42+196.
120development of an intelligent society.5.4 The "Intelligence Divide": The Emergence of a New Form of InequalityTraditional research on the "digital divide" has mainly concerned inequality inaccess to technology. The age of AI, however, is giving rise to a deeper form ofinequality—the "intelligence divide."The intelligence divide manifests along multiple dimensions: not only whetherone can use AI tools at all (the access layer), but also whether one can effectively useAI to enhance one's own abilities (the skill layer), and whether one can share in thegains of an AI-driven economic system (the outcome layer). Digital technology hasnot delivered inclusive empowerment—digitally disadvantaged groups do not equallyenjoy the digital dividends of emerging technology. When service channels aredigitized in a "one-size-fits-all" manner, or when online electronic service channelsare given priority, groups unable to keep pace with intelligent transformation aresystematically marginalized.Even more concerning is the "Matthew effect"—existing inequalities areamplified through the application of AI. Groups already in a position of advantage arebetter able to use AI to widen their lead, while disadvantaged groups fall furtherbehind for lack of the relevant skills and resources. Artificial intelligence may becomea force that widens, rather than narrows, social gaps.6. The Coupling Logic of the Dual Structural Transformation andGovernance Responses6.1 Coupling Logic: From Skill Substitution to Social RestructuringThe transformation of the employment structure and that of human socialstructure are not two parallel tracks, but are deeply coupled through a clear chain oftransmission.The first link is "skill substitution"—as AI replaces particular types of labor, thefirst thing it changes is the skill-demand structure of the labor market. Mid-skill
121positions shrink while high-skill positions expand, giving rise to "middle-tiercollapse." The second link is "income divergence"—changes in skill demandinevitably bring changes in income distribution. Displaced workers face fallingincomes or even unemployment, while high-end talent equipped with AI skills earn apremium, widening the income gap. The third link is "power restructuring"—thedivergence in income accumulates into a divergence in wealth, and the divergence inwealth translates into a divergence in power. Those able to steer the development ofAI technology and the rules governing its use gain the ability to define social order.The sustained concentration of technological dividends among different groupsultimately manifests as a restructuring of social structure, as shown in Figure 4.Figure 4 The "Skill-Income-Power" Transmission ChainThis chain reveals an important fact: AI's impact on society is not a "technicalproblem" but an "order problem." When a technology can simultaneously change whohas a job, who has money, and who has power, it is no longer merely a tool, but aforce reshaping order itself.6.2 Governance Response: Systematic Restructuring Along Three DimensionsFacing the dual shock to the employment structure and human social structure,governance responses cannot be piecemeal patchwork—they must be systematicrestructuring.First, restructuring the education system. AI's reshaping of skill demand requiresfundamental adjustment of the education system. Education in the future should notmerely transmit knowledge—since knowledge can be generated by AI—but shouldfocus on cultivating abilities that AI cannot replace: critical thinking, creativity,
122empathy, and the ability to solve complex problems. At the same time, alifelong-learning system must become social infrastructure, not a luxury available toonly a few.Second, transforming the social security system. Traditional social securitysystems face difficulties in identifying beneficiaries, adapting institutionally, andsharing technological dividends. Cai Fang has pointed out that the direction forimproving the social security system should be a shift from a system closer to"residual" toward one closer to "institutional." This means social security should notmerely be "relief" targeted at the unemployed, but should become a foundationalinstitutional arrangement covering all citizens, adapting to flexible forms ofemployment, and capable of withstanding the shocks of structural change. The shareof government social spending in GDP needs to rise, shifting from "investment inthings" toward greater "investment in people." New concepts, technologies, andmodels represented by "Internet Plus" and big data should be actively promoted forwide application in the field of unemployment services, accelerating the fulldigitization of unemployment-insurance services and management, andcomprehensively improving service and management capacity.11Third, innovating the governance paradigm. In the face of the social-structuralchanges triggered by AI, social governance systems urgently need to shift from"passive response" to "proactive adaptation." This includes: establishing a monitoringand early-warning mechanism for AI's impact on employment so as to anticipate itssocial consequences before technology diffuses; improving the income-distributionsystem and regulating the share of the labor factor in distribution; promoting themarketization and fair distribution of data as a factor of production; and activelyparticipating in global AI governance so as to secure a voice in the making of therules.11[11] Zhang Yinghua, Zhang Zhanli & Zheng Bingwen. (2019). Seventy years of unemploymentinsurance in New China: Historical evolution, problem analysis, and recommendations. SocialSecurity Studies, (6), 3-15.
1237. ConclusionThis paper has systematically analyzed the structural changes brought about byartificial intelligence along two dimensions—the employment structure and humansocial structure—and arrives mainly at the following conclusions.First, AI's reshaping of the employment structure unfolds simultaneously throughthree mechanisms—substitution, creation, and industrial-structuretransformation—and its impact follows an evolutionary trajectory from "polarization"to "upgrading." The most prominent structural risk at present is "middle-tier collapse"and "career-ladder fracture"—the shrinking of mid-skill positions and the reduction ofentry-level positions are changing the shape of the entire career ladder.Second, AI's impact on human social structure runs deeper still. It isrestructuring modes of knowledge production and cognition, intensifying theconcentration of wealth and power, reshaping the social division of labor andhuman-machine relations, and giving rise to a new form of inequality—the"intelligence divide." These changes have already gone beyond the purely economicdomain, touching the basic rules by which society operates.Third, the transformation of the employment structure and that of human socialstructure are deeply coupled through a "skill-income-power" transmission chain. AI'simpact is not a localized matter of "technical substitution," but a systemic"restructuring of order." Governance responses must undertake systematicrestructuring along three dimensions: the education system, social security, and thegovernance paradigm.The development of artificial intelligence is irreversible, and its reshaping ofsocial structure has only just begun. Faced with this historic transformation, we needto move beyond the shallow anxiety of "how many jobs will AI replace" and thinkdeeply about a more fundamental question: in an era in which AI is deeply embeddedin the functioning of society, how should human society redefine the value of "work,"the meaning of "knowledge," and the place of the "human being"? This is not merely
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International Journal of Responsible Artificial Intelligence ResearchISSN(online)3106-858Volume 2, Isstue 2 (2026)(Overall No.3)Publisher: LINGNAN SCIENTIFIC AND INDUSTRIAL PRESS CO., LTD.Editor-in-Chief: Alexander Y.J.SterlingSenior Editors: Jan Chen , Linyuan Xia, Chia Hsing Wang,Xin YangAddress: A91,3/F, Nan Yue Commercial Centre, Calcada de Santo Agostinho 19, MacauEmail: lingnansciaoutlook.comPrinter: Lingnan Scientific and IndustrialPress Co., Ltdl.Date: July 2026Place: Macao SAR,ChinaCapyright: Copyright O 2025 by LINGNAN SCIENTIFIC ANDINDUSTRIAL. PRESS CO, LITD.Allrighis reserved. No part of this publitation may be reproduced, stored in a retrieval system , or transmitted in any form or by any means, alectronic, mechanical, pholocopying, recording, or other-wise, without the prior permission of the pulbisher.ISSN (Prind)3106-857X8Price: 100 MOP
