A Multi-Layered, Progressive Model of Self-Driving Tourists’ Environmental Responsibility Behavior: Enriched Tourism Destination 6A Framework
Abstract
1. Introduction
2. Literature Review
2.1. Self-Driving Tours
2.2. Environmental Responsibility Behavior
2.3. Tourism Destination 6A Framework
3. Materials and Methods
3.1. Research Design
3.2. Research Site
3.3. Data Collection Administration
3.4. Data Analysis
4. Results
4.1. Open Coding
4.2. Axial Coding
4.3. Selective Coding
4.4. Theoretical Saturation Test
- (1)
- Adding five additional interviews during the later stage of coding (participants 21–25, including government officials, academics, industry practitioners, and local residents) to assess whether any new conceptual codes emerged.
- (2)
- Reanalyzing the frequencies and interrelationships of existing codes to confirm structural stability and coherence among the categories.
5. Discussion
5.1. Theoretical Contributions
- (1)
- Extending the 6A lens from triggers to a layered process. We position the classic 6A framework as the Basic Experience Layer that provides proximal experiential triggers but theorize that ERB becomes robust only when macro-governance structures and community-level culture/empathy are engaged. This reframes ERB formation from single-layer stimuli to a progressive, multi-layer process [69,74].
- (2)
- Articulating cross-level mechanisms and sequencing. We specify a three-step pathway—regulatory clarity → technological enablement → cultural internalization—that explains how destinations convert momentary pro-environmental intentions into enacted and persistent behaviors [29,70,71]. This sequencing operates through two complementary routes: an instrumental route that reduces price/friction via C1–C2, and a normative–affective route consolidated by C3. This sequencing aligns with recent findings on policy-supported PBC, low-friction digital nudges, and community contagion of ERB [69,70,71,73].
- (3)
- Boundary conditions for transferability. We propose that the effects are stronger under higher governance maturity, higher digital readiness, and stronger community cohesion. Recent studies corroborate that policy regimes and affect-laden experiences (e.g., awe) condition PEB formation [70,81]. These propositions invite future comparative tests across destination types and regulatory environments. Additionally, the influence of C3 is likely contingent on stakeholder alignment and perceived fairness within communities. Heterogeneous interests and power asymmetries can moderate empathy-driven pathways to ERB [77,80].
5.2. Practical Implications
- (1)
- Governance and capacity management. Destinations should pair clear rules with graduated enforcement (e.g., on-site guidance + soft penalties for first offenses) and implement capacity limits/time-phased reservations on sensitive sites. Such policy mixes are shown to narrow the intention–behavior gap and stabilize ERB [70].
- (2)
- Technological enablement (“design for low-effort green”). Deploy digital nudges in navigation and booking flows (default green routes/parking, refill prompts, waste-sorting cues), and use post-trip flashback nudges via chatbots to sustain spillover behaviors at home [71,72]. These tools specifically target friction and attentional limits that commonly block ERB enactment. Pair digital design with light economic instruments—deposit–refund, time-of-day/proximity parking fees, and micro-rewards in apps—to shift behavior with low burden [70,75,76].
- (3)
- Community-anchored empathy. Support programs where residents model visible PEB (e.g., attendant-led sorting, local volunteer crews) and micro-narratives that humanize ecosystem impacts; both mechanisms heighten empathy and pro-environmental atmosphere, which in turn lift tourists’ ERB [73,74]. Ethical guardrails (privacy, transparency, opt-out) should accompany all digital interventions. On the other hand, map stakeholders and salience; co-design programs with residents and SMEs; specify benefit-sharing and rotate volunteer workloads; and establish low-friction grievance and feedback channels. Such participatory and meta-governance measures help manage conflict and sustain community-anchored empathy [78,79].
5.3. Limitations and Future Research
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Code | Open Code Label | Frequency | Supporting Excerpt | Interviewer ID |
---|---|---|---|---|
A1 | Strict Environmental Regulations | 18 | “We have issued stricter vehicle restrictions and emission standards, equipped with smart monitoring and enforcement teams.” | 1 |
A2 | Joint Law Enforcement Mechanism | 15 | “We have established a joint enforcement mechanism with public security, transport, and tourism departments to jointly penalize serious environmental violations.” | 2 |
A3 | Tourist Credit Record | 12 | “Some serious violations are included in individuals’ tourism credit records, affecting their access to travel services in Lijiang.” | 1 |
A4 | Ecological Red Line Designation | 10 | “We have designated ecological red lines across the entire Lijiang region, clearly defining no-development zones.” | 5 |
A5 | Environmental Carrying Capacity Assessment | 9 | “We issue real-time alerts and divert visitor flow based on tourist volume to avoid excessive environmental pressure.” | 4 |
A6 | Multi-Stakeholder Collaboration | 14 | “We regularly hold environmental protection forums involving tourism enterprises and community residents to jointly develop action plans.” | 1 |
A7 | Ecological Compensation Mechanism | 8 | “For ecologically impacted areas due to tourism development, developers are required to conduct restoration or provide compensation.” | 5 |
A8 | Smart Tourism App | 16 | “The official tourism app provides real-time traffic updates and air quality index, and recommends green travel routes.” | 1 |
A9 | New Energy Charging Stations | 13 | “We piloted smart charging stations for new energy vehicles along popular self-driving routes and scenic entrances.” | 1 |
A10 | Smart Waste Sorting Systems | 11 | “Smart sorting equipment automatically recognizes garbage types and issues alerts based on waste volume.” | 1 |
A11 | Environmental Sensors | 9 | “We piloted the installation of smart sensors to monitor waste, water quality, and noise levels in real time.” | 2 |
A12 | AR-Based Environmental Tasks | 8 | “We designed AR-based environmental tasks where tourists can scan signs to learn about ecology and complete challenges.” | 4 |
A13 | Carbon Credit System | 7 | “We piloted a blockchain-based carbon credit system to encourage low-carbon travel and green activities.” | 5 |
A14 | Smart Parking System | 10 | “We piloted a smart parking management system allowing for advance reservations to reduce unnecessary idling and emissions.” | 3 |
A15 | Eco Navigation Alerts | 9 | “In collaboration with navigation companies, we marked ecologically sensitive areas and no-parking zones on maps.” | 2 |
A16 | Naxi Ecological Wisdom | 17 | “Naxi cultural values embody harmony between humans and nature, as reflected in everyday life.” | 3 |
A17 | Household Waste Sorting | 15 | “Residents in the old town sort waste meticulously, with some families composting food scraps themselves.” | 1 |
A18 | Community Environmental Role Models | 14 | “Tourists feel embarrassed to litter after witnessing residents’ strong environmental habits.” | 1 |
A19 | Eco-Friendly Homestays | 12 | “Eco-friendly homestay demonstration sites run by locals proactively share environmental stories with guests.” | 4 |
A20 | Cultural Experience Activities | 11 | “Visitors are invited to participate in traditional farming activities to learn about ecological agriculture.” | 1 |
A21 | Emotional Resonance | 13 | “When tourists see how much locals cherish their homeland, it evokes strong emotional resonance.” | 2 |
A22 | Ecological Interpreters | 10 | “Local residents act as ecological interpreters, sharing stories about land protection with visitors.” | 5 |
A23 | Environmental Storytelling | 9 | “Guesthouse owners tell stories about protecting the snow mountain, which inspires tourists’ sense of responsibility.” | 1 |
A24 | Volunteer Guidance | 8 | “Volunteers guide tourists in waste sorting and urban managers stop littering behaviors.” | 1 |
A25 | Green Cultural Products | 7 | “We promote handicrafts made from recycled materials that convey environmental values.” | 4 |
A26 | Environmental Point Incentives | 8 | “Tourists can earn points for eco-friendly behaviors and exchange them for souvenirs.” | 11 |
A27 | Leave-No-Trace Camping | 7 | “We emphasize the leave-no-trace concept to reduce environmental footprints during camping.” | 11 |
A28 | Eco-Driving Challenge Events | 6 | “We organize eco-driving challenges that record fuel-saving and waste-sorting performance.” | 13 |
A29 | Environmental Training | 8 | “The government conducts environmental training to communicate policy and technical requirements to practitioners.” | 11 |
A30 | Blacklist System | 7 | “Tourists who damage the environment are blacklisted and restricted from re-entering parks.” | 15 |
Axial Code | Included Open Codes | Logical Relationship Explanation | Frequency |
---|---|---|---|
B1: Policy, Regulation, and Collaborative Governance | A1, A2, A3, A4, A5, A6, A7, A24, A29, A30 | Through strict regulations, joint law enforcement, and multi-stakeholder collaboration, an institutional constraint and social supervision system is formed to regulate tourist behavior. | 101 |
B2: Smart Technology and Green Innovation | A8, A9, A10, A11, A12, A13, A14, A15, A26, A27, A28 | By leveraging smart technologies and incentive mechanisms, the cost of engaging in ERB is reduced, thereby enhancing tourist participation. | 94 |
B3: Community Culture and Emotional Resonance | A16, A17, A18, A19, A20, A21, A22, A23, A25 | Through cultural transmission, local resident role modeling, and emotional interaction, tourists are inspired to resonate with and internalize environmental values. | 103 |
Selective Code | Included Axial Codes | Included Open Codes | Relationship Explanation |
---|---|---|---|
C1: Destination Governance Capacity for Sustainability | B1 | A1, A2, A3, A4, A5, A6, A7, A24, A29, A30 | Through regulatory constraints, multi-stakeholder collaboration, and social oversight, a systematic governance structure is established to regulate tourist behavior. |
C2: Green Innovation Practices at the Destination | B2 | A8, A9, A10, A11, A12, A13, A14, A15, A26, A27, A28 | By leveraging smart technologies and incentive mechanisms, the cost of ERB is reduced, enhancing tourists’ willingness to participate. |
C3: Community-Based Environmental Empathy | B3 | A16, A17, A18, A19, A20, A21, A22, A23, A25 | Through cultural transmission, resident role modeling, and emotional interaction, tourists are inspired to resonate with and internalize environmental values. |
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Sort | Count | Centrality | Keywords |
---|---|---|---|
1 | 38 | 0.17 | Sustainable development |
2 | 38 | 0.09 | Management |
3 | 37 | 0.13 | Sustainable tourism |
4 | 25 | 0.14 | Tourism |
5 | 24 | 0.46 | Conservation |
6 | 15 | 0.04 | Model |
7 | 15 | 0.27 | Protected areas |
8 | 15 | 0.26 | Attitudes |
9 | 15 | 0.03 | Satisfaction |
10 | 14 | 0.05 | Impact |
Rank | Province | Region | Rank | Province | Region |
---|---|---|---|---|---|
1 | Yunnan | outhwest | 11 | Guangxi | South China |
2 | Guangdong | South China | 12 | Gansu | Northwest |
3 | Sichuan | Southwest | 13 | Jiangsu | East China |
4 | Beijing | North China | 14 | Hunan | Central China |
5 | Zhejiang | East China | 15 | Fujian | East China |
6 | Shandong | East China | 16 | Hubei | Central China |
7 | Inner Mongolia | North China | 17 | Hainan | South China |
8 | Shaanxi | Northwest | 18 | Jiangxi | East China |
9 | Xinjiang | Northwest | 19 | Shanxi | North China |
10 | Guizhou | Southwest | 20 | Chongqing | Southwest |
Rank | Location | Visitors |
---|---|---|
1 | Lijiang | 38,461 |
2 | Kunming | 33,627 |
3 | Dali | 28,781 |
4 | Shangri-La | 17,408 |
5 | Shuhe | 15,596 |
6 | Xishuangbanna | 8543 |
7 | Shuanglang | 7313 |
8 | Diqing | 6738 |
8 | Tengchong | 4846 |
9 | Deqen County | 4714 |
10 | Chuxiong | 4531 |
Interviewee Type | Number | ID | Professional Role | Average Years of Experience | Key Contributions to Research |
---|---|---|---|---|---|
Government Officials/Tourism Management | 5 | 1–5 | Director/Bureau Chief Community-Level Personnel | 15 years (10–25) | Macro-level policies, regulations, inter-departmental coordination, and governance capacity. |
Academic Scholars/Researchers | 5 | 6–10 | Professor/Research Fellow | 12 years (8–20) | Theoretical insights, environmental psychology, sustainable tourism trends, and behavioral economics. |
Tourism Industry Practitioners | 5 | 11–15 | Operator/Manager/Supervisor | 10 years (7–18) | Industry practices, technological adoption, business strategies, market feedback, and operational challenges. |
Local Community Representatives | 5 | 16–20 | Committee Head/Village Elder/NGO Leader/Local residents | 20 years (15–30) | Local culture, resident behavior, community initiatives, traditional ecological wisdom, and social dynamics. |
Total | 20 |
Testing Stage | Data Source | New Codes Identified | Frequency Change | Results |
---|---|---|---|---|
Initial Coding (Participants 1–20) | 20 interviews | A1–A30 | Total frequency: 600 | Formed 30 open codes, 3 axial codes, and 3 core categories |
Supplementary Coding (Participants 21–25) | 5 interviews | None | Approx. 15% increase (e.g., A1 + 2, A8 + 3, etc.) | No new codes emerged; existing category relationships remained stable |
Category Relationship Review | Reanalysis of the entire dataset | None | No change in logical relationships | Complementarity among core categories was further reinforced |
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Tong, X.; Phakdeephirot, N.; Jiang, S. A Multi-Layered, Progressive Model of Self-Driving Tourists’ Environmental Responsibility Behavior: Enriched Tourism Destination 6A Framework. Sustainability 2025, 17, 8786. https://doi.org/10.3390/su17198786
Tong X, Phakdeephirot N, Jiang S. A Multi-Layered, Progressive Model of Self-Driving Tourists’ Environmental Responsibility Behavior: Enriched Tourism Destination 6A Framework. Sustainability. 2025; 17(19):8786. https://doi.org/10.3390/su17198786
Chicago/Turabian StyleTong, Xinyang, Nutteera Phakdeephirot, and Songyu Jiang. 2025. "A Multi-Layered, Progressive Model of Self-Driving Tourists’ Environmental Responsibility Behavior: Enriched Tourism Destination 6A Framework" Sustainability 17, no. 19: 8786. https://doi.org/10.3390/su17198786
APA StyleTong, X., Phakdeephirot, N., & Jiang, S. (2025). A Multi-Layered, Progressive Model of Self-Driving Tourists’ Environmental Responsibility Behavior: Enriched Tourism Destination 6A Framework. Sustainability, 17(19), 8786. https://doi.org/10.3390/su17198786