A Study of Cognitive-Affective Pathways in Societal Adoption of Emerging Technologies : Evidence from the Low-Altitude Economy

국문 초록 (Abstract)

비행자동차, 드론 등 저공비행 기술의 급속한 발전에 따라 저공경제는 미래 산업 전환과 도시공간 재구성을 주도하는 핵심 동력으로 부상하고 있다. 세계적으로 미국, 유럽연합, 일본, 한국 등 주요 경제권은 저공경제를 미래 도시 항공교통과 스마트 모빌리티 체계의 핵심 구성요소로 인식하고 있으며, 관련 정책과 산업 전략이 빠르게 전개되고 있다. 중국 또한 국가 전략 차원에서 저공경제 시범구역을 추진하고 있어, 글로벌 저공산업 경쟁 구도가 본격적으로 형성되고 있음을 시사한다. 그러나 이러한 저공경제는 전형적인 신흥 사회기술시스템으로서, 대중의 수용 수준에는 여전히 높은 불확실성이 존재한다. 이에 따라 대중이 저공경제를 어떻게 인식하고, 어떠한 심리적·행동적 메커니즘을 통해 수용하는지를 규명하는 것은 정책 실행과 시장 형성을 위한 핵심 과제가 되고 있다.
본 연구는 이론적·방법론적 측면에서 융합적 혁신을 시도하였다. ‘인지–정서–행동(Cognition–Affect–Behavior, CAB)’의 3단계 메커니즘을 기반으로, “LDA 주제모형 + BERT 감정분석 + SEM 구조방정식모형”을 결합한 통합 연구 프레임워크를 구축하였다. 이를 통해 자연언어 환경에서 대중이 저공경제에 대해 보이는 실제 관심 주제와 행동 의향의 논리를 규명하고자 하였다. 구체적으로, 2024–2025년 동안 소셜미디어 플랫폼에서 수집한 약 6만 건의 ‘저공경제’ 관련 댓글 데이터를 분석하였다. LDA 모형을 통해 ‘지각된 위험’, ‘정책 인식’, ‘생활 편의성’, ‘시장 신뢰’, ‘정보 투명성’, ‘기술 수용도’, ‘지각된 비용’의 7개 핵심 인지 주제를 도출하였다. 이후 BERT 중국어 감정분석 모형을 활용하여 감정 경향을 식별한 결과, 긍정 감정이 68.1%, 부정 감정이 31.9%로 나타났으며, 주제별로 뚜렷한 차이를 보였다. 마지막으로 LDA와 BERT의 분석 결과를 CAB 이론 하의 변수 체계로 매핑하고, 유효 설문 847부의 데이터를 기반으로 SEM 모형을 구축하여 각 인지 요인이 대중의 사용 의향에 미치는 직접·간접 경로 효과를 검증하였다.
실증분석 결과, (1) 대중의 관심은 안전성, 친환경성, 비용의 세 가지 핵심 의제로 집중되어 있으며, 이는 정책적 우선 관리 영역으로 확인되었다. (2) 기술 수용도(Technology Acceptance)는 행동의향에 유의한 정(+)의 영향을 미치지만, 위험 인식이 강한 상황에서는 이중적 정서 반응을 유발할 수 있어 ‘기술의 양날의 검 효과(dual-edged effect)’를 보였다. (3) 정서 변수는 인지와 행동 사이에서 유의한 매개효과를 가지며, CAB 이론의 연쇄적 인과 메커니즘을 실증적으로 지지하였다. (4) LDA와 BERT의 결합은 비정형 텍스트 데이터를 구조화된 인과모형으로 전환하는 방법론적 혁신을 실현하였다.
본 연구의 주요 학문적 기여는 다음과 같다. 첫째, 이론적으로 CAB 이론의 적용 범위를 신흥 기술의 사회적 수용 분야로 확장함으로써, 대중의 인지–정서 상호작용에 기반한 행동결정 메커니즘을 규명하였다. 둘째, 방법론적으로는 의미 분석에서 구조모형으로 이어지는 다층적 통합 분석 패러다임을 제시하여, 심리적 변수의 정량화에 새로운 접근을 제안하였다. 셋째, 실무적으로는 연구 결과가 정부의 저공경제 관련 정책 수립, 대중 커뮤니케이션 전략 개선, 위험 인식 관리 체계 구축에 실질적 근거를 제공한다.
끝으로, 본 연구는 표본 구조, 정서 차원, 방법론 융합 측면에서의 한계를 인식하며, 향후 공간적 행동 데이터의 결합, 도시 간 비교, 동태적 진화 관점을 포함한 확장 연구를 통해 저공경제의 대중 수용 연구의 이론적·실증적 기반을 심화할 것을 제안한다.

다국어 초록 (Multilingual Abstract)

With the rapid advancement of low-altitude flight technologies such as flying cars and drones, the low-altitude economy has gradually become a key driver for future industrial transformation and urban spatial restructuring. Globally, major economies such as the United States, the European Union, Japan, and South Korea have all positioned the low-altitude economy as a key component of future urban air mobility and intelligent transportation systems, with related policies and industrial deployments accelerating steadily. China, at the level of national strategy, is also promoting the establishment of low-altitude economy pilot zones, marking the accelerated formation of global competition within the emerging low-altitude industry landscape.
However, as a typical emerging socio-technical system, its level of public acceptance remains highly uncertain. Understanding the public’s cognitive structure and behavioral mechanisms regarding the low-altitude economy has thus become a central issue for policy implementation and market development.
This study achieves theoretical and methodological integration by developing a unified research framework—“LDA topic modeling + BERT sentiment analysis + SEM structural equation modeling”—grounded in the three-stage Cognition–Affect–Behavior (CAB) mechanism, aiming to uncover the public’s genuine concerns and behavioral intentions toward the low-altitude economy in natural contexts. Using web crawling techniques, more than 60,000 social media comments on the “low-altitude economy” from 2024–2025 were collected, and the LDA model identified seven key cognitive themes: perceived risk, policy perception, life convenience, market trust, information transparency, technological acceptance, and perceived cost. Subsequently, sentiment tendencies were identified using the Chinese BERT sentiment analysis model, revealing that 68.1% of sentiments were positive and 31.9% negative, with significant variations across different topics. Finally, the analytical results from LDA and BERT were mapped into a variable system under the CAB theoretical framework, and an SEM model was built using 847 valid survey responses to examine the direct and indirect pathway effects of cognitive factors on public usage intention.
Empirical findings indicate that (1) public attention is highly concentrated on three major topics—safety, sustainability, and cost—which should be prioritized in policy governance; (2) although technological acceptance (TA) exerts a significant positive effect on behavioral intention, it may simultaneously evoke ambivalent emotional responses under strong perceived risk conditions, reflecting a “double-edged sword effect” of technology; (3) affective variables play a significant mediating role between cognition and behavior, validating the chain mechanism proposed by CAB theory; and (4) the integration of LDA and BERT enables the transformation of unstructured textual data into structured causal modeling, providing a novel methodological pathway for studying socio-technical adoption.
The main contributions of this study are as follows: Theoretically, it extends the applicability of CAB theory to the field of societal acceptance of emerging technologies, revealing the behavioral decision-making mechanisms shaped by the interaction between cognition and emotion. Methodologically, it proposes a cross-level integration paradigm from semantic mining to structural modeling, offering new insights into the quantification of psychological variables. Practically, the findings provide empirical evidence for governments to formulate low-altitude economy policies, optimize public communication mechanisms, and improve risk perception management systems.
Finally, the study acknowledges limitations related to sample structure, emotional dimensions, and methodological integration, and suggests that future research incorporate spatial behavioral data, cross-city comparisons, and dynamic evolutionary perspectives to further refine the theoretical and empirical foundations of public acceptance studies on the low-altitude economy.

목차 (Table of Contents)

• Ⅰ. Introduction 1
• 1.1 Research Background 1
• 1.2 Research Significance 2
• 1.3 Methodological Innovation and Technical Pathway 3
• 1.4 Core Research Questions 5
• 1.5 Research Objectives and Process 5
• 1.5.1 Research Objectives 5
• 1.5.2 Research Design and Process 6
• 1.6 Structure of the Dissertation 7
• 1.7 Research Innovations 9
• Ⅱ. Literature Review 11
• 2.1 Academic Progress in Low-Altitude Economy Research 11
• 2.2 Studies on Factors Influencing Usage Intention 12
• 2.3 Application of LDA Topic Modeling in User Cognition Research 14
• 2.4 BERT Model and Construction of Emotional Variables 15
• 2.5 The Integrated Path of LDA + BERT + SEM from the CAB Theory Perspective 17
• 2.5.1 Integration Framework 18
• 2.5.2 Research Procedure 18
• 2.5.3 Advantages of the Integrated Pathway 18
• 2.5.4 Application Value in the Low-Altitude Economy Context 19
• 2.6 Methodological Innovations and Differences from Existing Literature 19
• 2.6.1 Text-Driven Cognition Variable Identification Mechanism (Cognition) 19
• 2.6.2 Construction of Affective Mediation Mechanism (Affect) 20
• 2.6.3 Tri-Dimensional Method Integration and Unified CAB Modeling 20
• 2.6.4 Extension of CAB Paradigm to Emerging Technology Applications (Behavior) 21
• 2.7 Theoretical Foundation 21
• 2.7.1 Core Concepts of CAB Theory 22
• 2.7.2 Applicability of CAB Theory to the Low-Altitude Economy 22
• 2.7.3 Alignment of CAB Theory with the LDA–BERT–SEM Framework 23
• 2.7.4 Comparative Advantages of CAB Theory 23
• 2.7.5 Summary 24
• Ⅲ. Hypothesis Development 25
• 3.1 Perceived Risk (PR) 25
• 3.2 Policy Cognition (PC) 26
• 3.3 Perceived Convenience (PV) 28
• 3.4 Market Trust (MT) 29
• 3.5 Information Transparency (IT) 31
• 3.6 Technology Acceptance (TA) 32
• 3.7 Perceived Cost (PCO) 33
• 3.8 Positive Affect (PA) 35
• 3.9 Negative Affect (NA) 36
• 3.10 Behavioral Intention (BI) 37
• Ⅳ. Research Methods 41
• 4.1 Research Design 41
• 4.2 Design of the Low-Altitude Economy Usage Intention Scale 42
• 4.2.1 Demographic Variables 42
• 4.2.2 Core Scale Based on CAB Dimensions 43
• 4.2.3 Scoring and Reliability 43
• 4.3 Sample Selection 45
• Ⅴ. Text Mining and Sentiment Analysis of Online Comments 48
• 5.1 Data Collection and Text Preprocessing 48
• 5.1.1 Data Sources 48
• 5.1.2 Text Preprocessing 49
• 5.1.3 Text Preprocessing and Standardization Procedure 51
• 5.2 Word Cloud Analysis Method and Results 53
• 5.3 Word Cloud Analysis and Findings 55
• 5.4 LDA Topic Modeling Method and Result Analysis 57
• 5.4.1 Topic Coherence Analysis (Coherence Score) 58
• 5.4.2 Determining the Optimal Number of Topics 59
• 5.4.3 Model Perplexity Analysis (Perplexity) 60
• 5.4.4 Model Perplexity Analysis 62
• 5.4.5 Topic Visualization Analysis Based on PyLDAvis 63
• 5.4.6 Validation of the Seven-Topic LDA Model 65
• 5.4.7 Comprehensive Evaluation and Determination of Topic Number 66
• 5.5 Extraction of Thematic Factors Related to Willingness to Use the Low-Altitude Economy 68
• 5.5.1 Topic 1: Perceived Risk 70
• 5.5.2 Topic 2: Policy Cognition 71
• 5.5.3 Topic 3: Perceived Convenience 72
• 5.5.4 Topic 4: Market Trust 72
• 5.5.5 Topic 5: Information Transparency 73
• 5.5.6 Topic 6: Technology Acceptance 74
• 5.5.7 Topic 7: Perceived Cost 75
• 5.6 BERT-Based Sentiment Analysis 77
• 5.6.1 Model Selection and Analytical Method 77
• 5.6.2 Definition of Emotion Labels 80
• 5.7 Cross-Analysis of LDA Topics and BERT-Based Emotions 83
• 5.7.1 Cross-Results between LDA Topics and BERT Sentiments 83
• 5.7.2 Significance of Emotion–Topic Integration 85
• 5.8 Chapter Summary 88
• 5.8.1 Cognitive Layer 88
• 5.8.2 Affective Layer 89
• 5.8.3 Cognition–Affect Interaction 89
• 5.8.4 Theoretical Integration and Research Significance 90
• Ⅵ. Public Attitudes and Acceptance Toward the Low-Altitude Economy 91
• 6.1 Questionnaire Design and Empirical Modeling Framework Based on CAB Theory 91
• 6.2 Indicator System Construction Based on AHP 95
• 6.3 Sample Characteristics and Distribution 100
• 6.4 Reliability Analysis 106
• 6.5 Validity Analysis 107
• 6.6 Multicollinearity Test 118
• 6.7 Structural Equation Modeling (SEM) 119
• 6.8 Chapter Summary 126
• Ⅶ. Conclusion 128
• 7.1 Cognition–Affect–Behavior Decision Mechanism 129
• 7.2 Key Empirical Findings 133
• 7.3 Chapter Summary 136
• Ⅷ. Policy Recommendations 137
• 8.1 Government Level 137
• 8.2 Enterprise Level 139
• 8.3 Public Level 142
• 8.4 Data-Driven Co-Constructive Governance Logic 145
• Ⅸ. Research Limitations and Future Directions 149
• 9.1 Research Limitations 149
• 9.2 Future Research Directions 150
• 9.3 Summary 151
• References 153
• 국문요지 166