Submitted:
29 May 2025
Posted:
30 May 2025
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Abstract
Keywords:
1. Introduction
2. Definitions
3. Problem of the Study
4. Justification of the Study
5. Importance of the Study
6. Theoretical Background: Some Trends of Drone Ethical Dilemmas
7. What Are Drones’ Impacts on Society?
8. Commercial and Humanitarian Drones as Bright Examples of Drone Applications
8.1. Commercial Drones and Their Societal Impact
8.2. The Enormous Humanitarian Potential of Drones
9. Some Notable Ethical Risks of Drones
9.1. Safety Concerns
9.2. Legislative Uncertainties
9.3. Privacy Issues
9.4. Software Concerns and Malfunctions
9.5. Spying Issues
9.6. The Threat of Being Easy to Hack
9.7. Threat to Tourist Business
10. Some Factors Impacting Drone Acceptance
11. Suggested Solutions: Areas of Concentration to Foster Drone Societal Impacts
11.1. Community Engagement
11.2. Policy and Regulation
Regulatory Frameworks
11.3. Gaining and Developing Skills
11.3.1. STEM Education
11.3.2. Community Training
11.4. Environmental Protection
11.4.1. Animal Monitoring
11.4.2. Medical Supply Transport
11.4.3. Emergency Response
11.4.4. Handling of Disasters
11.4.5. Damage Assessment
11.5. Agriculture Monitoring and Infrastructure
11.6. Moral Deliberations
11.6.1. Privacy Protection
12. Research Methodology
12.1. The Reasons for Adopting the Interpretivist Paradigm
12.2. Research Context and Participant Recruitment
12.3. Interview Content
- A checklist on the predicted level of public approval for some drone applications, such as research, disaster management, medical purposes, agriculture, military purposes, passenger transport, civil protection, energy supply, parcel delivery, hobby, photos, videos, and films.
- A checklist of the acceptance of the seriousness of some known drone concerns, such as growing warfare threats, violation of privacy, technology maturity, noise, misuse of criminal actions, doubts regarding accountability and insurance, potential damage and injury, unclarity of legal regulations, negative public perceptions, traffic concerns such as congested skies and endangerment of road traffic and low-cost increase easiness of acquisitions.
12.4. Data Collection
12.5. Data Analysis
12.5.1. Phase One
- Study the transcripts attentively to familiarize ourselves with the facts.
- To verify their relevance and coherence, create initial codes based on theoretical ideas before applying them to raw data.
- Organize emergent ideas using a theoretical framework.
- Finalize themes and related topics: We followed the conceptual structure and made final selections.
- Continue to analyze the data until no new noteworthy patterns arise.
- Ensure all interviews include thorough theme development and in-depth data collection.
12.5.2. Phase Two
- Remove any prejudices from the initial code cycle.
- Compare the updated codes to the originals to identify inconsistencies and ensure consistent coding.
- Review contradictions and reach final judgments.
- Evaluate the reliability of the first coding and reaffirm the dependability of the results.
12.5.3. Phase Three
- Select extracts and sample excerpts from interviews to demonstrate themes and ensure that the data accurately reflects the participants’ perspectives.
- Prepare the final theme analysis report, verifying that the findings are consistent and indicative of the data.
12.6. How the Results Were Obtained
- What factors impact the predicted level of public approval for drone applications?
- What are the main factors impacting how approval ratings for different drone applications relate?
- Do public approval ratings vary depending on each drone application?
- What are the reactions to acknowledging the gravity of some well-known drone issues, such as the increasing threat of war and privacy violations?
12.7. Methodology Used for ANOVA Analysis
12.7.1. Steps
- Data Preparation: The dataset is divided into groups based on the participants’ years of expertise (e.g., 5-10 years, 10-20 years, etc.).
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Define Dependent and Independent Variables:
- o
- IDependent variables: Approval ratings for each drone application (e.g., Research, Medical purposes).
- o
- IIndependent variable: Years of expertise (categorical variable).
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Assumptions Check: Ensure the data meets ANOVA assumptions:
- o
- IThe data should be normally distributed within groups.
- o
- IHomogeneity of variance: The variance within each group should be similar.
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Conduct ANOVA: Use a one-way ANOVA to compare the mean approval ratings of drone applications across different expertise levels.
- o
- INull Hypothesis (H0): There is no difference in mean approval ratings across expertise levels.
- o
- IAlternative Hypothesis (H1): There is a difference in mean approval ratings across expertise levels.
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Interpretation:
- o
- If the p-value is less than 0.05, reject the null hypothesis, indicating a significant difference between groups.
- o
- IIf the p-value is greater than 0.05, fail to reject the null hypothesis, indicating no significant difference between groups.
Software/Tools Used: Python (using scipy.stats for ANOVA).
12.7.2. Correlation Analysis Methodology
12.7.3. Steps
- Data Preparation: Extract the approval ratings for each drone application (e.g., Research, Military purposes) from the dataset.
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Define Variables:
- o
- IAll drone applications are considered independent variables to identify relationships between them.
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Calculate Correlation Coefficients: Use Pearson’s correlation coefficient (r) to quantify the linear relationship between two variables.
- o
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IThe Pearson correlation coefficient ranges from -1 to +1:
- ▪
- +1: A perfect positive relationship (as one variable increases, the other increases).
- ▪
- -1: A perfect negative relationship (as one variable increases, the other decreases).
- ▪
- 0: No linear relationship.
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Interpretation:
- o
- IStrong positive correlation: Values close to +1 indicate that the approval for two drone applications increases together.
- o
- IStrong negative correlation: Values close to -1 indicate that as approval for one application increases, the other decreases.
- o
- IWeak or no correlation: Values near 0 suggest no significant linear relationship.
Software/Tools Used: Python (using Pandas to calculate the Pearson correlation matrix).
12.7.4. Summary of Methods
- ANOVA was used to test whether years of expertise significantly affected the mean approval ratings for each drone application. The method compares means between different groups to find significant differences.
- Correlation Analysis measured how approval ratings for different drone applications related to each other. This technique helps uncover how public attitudes toward one application might influence their views on others [67].
13. The Results
13.1. The Average Public Approval Ratings
- Disaster Management: 8.87
- Agriculture: 8.78
- Research: 8.70
- Photos, videos, and films: 8.04
- Military purposes: 7.91
- Parcel delivery: 7.74
- Hobby: 7.13
- Civil protection: 6.65
- Medical purposes: 6.61
- Energy supply: 6.13
- Passenger transport: 5.22

13.2. Interpretation
- Despite the growing ethical and psychological risks associated with drone warfare, these risks were not represented in the responses. Drones were perceived as helpful instruments rather than disruptive technologies or security threats [70].
- Highly Approved Applications: Disaster management (8.87), agriculture (8.78), and research (8.70) are highly accepted, likely due to their clear societal benefits.
- Moderate Approval: Medical purposes (6.61), military purposes (7.91), parcel delivery (7.74), and photos/videos (8.04) have moderate approval, reflecting a balance between perceived usefulness and concerns over safety, ethics, or privacy.
- Lower Approval: Passenger transport (5.22), civil protection (6.65), and energy supply (6.13) show lower approval, likely due to concerns about safety, privacy issues, or unfamiliarity.
13.3. General Insights
- Public Perception and Safety: Applications directly contributing to safety and societal benefits (e.g., disaster management, research) generally receive high approval.
- Privacy and Ethical Concerns: Applications involving drones in public spaces or for personal use (e.g., civil protection, passenger transport) may raise concerns over privacy, security, and ethics, resulting in lower approval ratings.
- Unfamiliarity or Mistrust: Lower ratings for energy supply and passenger transport suggest that the public may be unfamiliar with these applications or lack sufficient trust in the technology for such critical tasks.
13.4. Insights Based on the Main Factors Impacting Drone Acceptance
13.5. Insights Based on Participants’ Years of Expertise
- ▪
- 5-10 Years: This group supported drone applications like disaster management and research while being more cautious about military purposes and civil protection.
- ▪
- 10-20 Years: Participants in this group displayed balanced approval across most applications, with notably lower ratings for passenger transport, reflecting caution in areas where public safety is a concern.
- ▪
- 20-30 Years: This group favored disaster management, agriculture, and parcel delivery but was more skeptical about passenger transport and energy supply.
- ▪
- 30-40 Years: The most experienced participants were generally positive, especially for medical purposes and military uses, suggesting more confidence in advanced applications, but they were less enthusiastic about photos/videos and hobby drones.
14. Advanced Statistical Analysis
14.1. ANOVA (Analysis of Variance)

14.2. The Implications of the Advanced Statistics
- No drone applications have statistically significant differences across the years of expertise, as all the p-values are above 0.05.
- Passenger transport (F-value: 1.89, p-value: 0.166) and Agriculture (F-value: 1.84, p-value: 0.175) show relatively higher F-values, indicating some differences in approval across experience levels, but not enough to be statistically significant.
- Applications such as Research (p-value: 0.72) and Disaster Management (p-value: 0.74) exhibit very low F-values, indicating that the ratings are pretty consistent across different experience levels.
14.3. Visual Representation of the Result That No Drone Applications Have Statistically Significant Differences Across the Years of Expertise
- (1)
- F-values show the variance ratio between groups to the variance within groups.
- (2)
- P-values indicate the differences in statistical significance.
15. Implications of the Correlation Analysis
15.1. Strong Positive Correlations
- ▪
- Military purposes and Photos, videos, and films (0.73): Participants who rated military purposes highly also favored using drones for photos and videos.
- ▪
- Civil Protection and Energy Supply (0.61): A strong relationship exists between approval for drones in civil protection and energy supply, possibly reflecting a belief in the utility of drones in infrastructure and security.
- ▪
- Passenger transport and Medical purposes (0.56): Participants who were positive about passenger transport also supported drones for medical purposes, indicating trust in drones for high-stakes applications.
15.2. Moderate Negative Correlations
- ▪
- Research and Medical purposes (-0.43): Participants who rated drones highly for research purposes were more skeptical of their use in medical contexts.
- ▪
- Photos, videos, and films, and Energy supply (-0.43): A moderate negative relationship suggests that those who approve of drones for media may not favor their use in infrastructure services.
15.3. Other Notable Correlations
- ▪
- Passenger transport and Civil protection (0.56): A positive correlation indicates that participants who support drones in transport also believe in their effectiveness for civil protection.
- ▪
- Photos, videos, and films (0.38): Those who support drones for media applications also tend to favor their use for hobbies, demonstrating alignment in personal and creative drone usage.
16. The Main Results of Both Analyses
- Consistency Across Expertise Levels: Both the ANOVA and regression analyses indicate that years of expertise do not significantly influence approval ratings. This suggests that the perception of drone applications is shaped more by the nature of the application than by professional experience.
- Application-Specific Relationships: The correlation analysis reveals interesting relationships between different drone applications, highlighting how certain use cases, such as military and media or civil protection and energy supply, tend to be perceived together. These correlations suggest a broader mindset, where some participants view drones as versatile tools for safety, while others associate them with creative or commercial uses.
- Public Perception Trends: Participants are generally more supportive of drones in areas with clear societal benefits, such as disaster management, research, and agriculture. However, there is more skepticism about drones in personal or controversial applications (e.g., passenger transport and energy supply).
17. Limitations of the Study
- Privacy concerns spark discussions around data usage and permission. Concerns regarding the specific challenges of drone technology may arise due to legal and procedural issues, which are not examined in this research. Regulations that lessen concerns about drone fear are desperately needed. However, the current study did not address legal issues or provide recommendations for controlling them.
- The research may be limited in focusing solely on scientists’ opinions on drone application and acceptability rather than the general public. Potential bias of experts toward particular applications, depending on their field of expertise, may be considered a limitation of our expert interview research.
- Since the creation of frameworks controlling the future use of drones depends on the degree of public acceptability, the authors attempt to exercise caution when implying potential issues that would be difficult to pinpoint without precise benchmarks.
- Despite our efforts to incorporate some elements that influence drone acceptability and applications, further research is required to examine other related issues. Although the issue of ethicality has been covered, burden, perceived effectiveness, intervention coherence, and self-efficacy are examples of different components that make up the theoretical framework of acceptability (TFA).
18. Future Considerations
- This new research is intended to understand drones’ potential negative social implications and develop suitable mitigating strategies. The sense of societal success will increase public acceptance and credibility of drones, promoting practitioners’ confidence and favorable scientific outcomes.
- Future studies should investigate the most effective ways to provide precise information on drones, including their types, potential risks, and advantages, as people are often eager to learn more about them and the outcomes of fulfilling their information requests to alleviate concerns about drones.
- The benefits of drone applications need more studies. To eliminate misconceptions about drones, a more effective study is required. Generally speaking, people assume that all drones are equipped with AI and are far more sophisticated and powerful than they actually are.
- One of the biggest concerns with drones is the potential for privacy violations. Highly fruitful research based on real-life experience is lacking. Because they perceive drones as flying robots violating their privacy, some may harbor unfounded fears, preventing them from being widely accepted.
- There is a growing need for more attention to future ethical challenges and how they might be addressed as drones become increasingly integrated into daily life.
- One of the key areas of future improvements in drone technology is the integration of AI and autonomous drones.
- A code of ethics for drone operators must be established for ethical monitoring and responsibility.
- There is a growing need for in-depth research on drone ethics because technology may develop more rapidly than the ethical frameworks for drones.
- The balance between moral dilemmas and technological development provides potential solutions or areas for further study.
- A potential future crucial problem that requires thorough investigation, particularly about international treaties, legal issues, and ethical standards, is how to regulate drone technology and utilize it to strike a balance between innovation and responsibility. Governments and businesses must ensure that drones are appropriately used, which is tied to military and corporate accountability. The humanitarian and severe psychological, social, and ethical implications of using drones in warfare need more research to be highlighted. There is an increasing demand for specialist studies on the possible psychological impact of armed drones on operators, target populations, and societies [71].
19. A Special Remark on How to Address the Potential Social Impact of Drones and Anti-Drones
19.1. Drones’ Notable Benefits Include
- Public safety and emergency response: Drones help manage disasters, flood surveillance, firefighting, and search and rescue. Medical supplies delivered to isolated or emergency areas improve access to healthcare.
- Economic Growth and Job Creation: New industries in drone maintenance, manufacturing, and services create skilled jobs, boosting media, logistics, infrastructure inspection, and agriculture output.
- Environmental Monitoring: Maintaining forests with a small ecological imprint, detecting pollutants, and safeguarding animals.
- Smart City Development: Incorporated into infrastructure assessment, traffic monitoring, and urban planning.
19.2. Notable Drawbacks of Drones
- Privacy Invasion: Unauthorized monitoring and data gathering are major worries associated with civilian drones.
- Risks to Security: Terrorists and non-state groups have turned drones into weapons, raising the possibility of asymmetric warfare and open assaults. Small drones make sabotage, surveillance, and smuggling more practical.
- Safety and Airspace Risks: The possibility of mid-air collisions with airplanes, particularly close to airports and in metropolitan areas. Unauthorized planes violate no-fly zones and put onlookers in danger [72].
19.3. Significance of Emphasizing Anti-Drone Technology
19.4. Significant Impacts of Anti-Drone Technology
- Strengthening Public Security: Anti-drone systems safeguard against harmful drone activity in critical locations (airports, military installations, and public gatherings) and ensure safe airspace in conflict areas or during high-profile events.
- Moral and Legal Conundrums: There are legal concerns when intercepting or disarming drones: Who owns the airspace? What happens if defenseless drones are destroyed? There is a possibility of abuse or overreach by the government.
- Issues with Civil Liberties: Radar and RF tracking are surveillance-based anti-drone devices that might increase society’s acceptance of invasive monitoring.
- The Rise in Militarization: The proliferation of drones and countermeasures might hasten arms races and normalize ongoing monitoring in daily life [75].
19.5. Notable Societal Balancing Act
- The public’s perception of drones changes according to the visibility of their use cases: commercial or military surveillance raises suspicions, while humanitarian uses encourage acceptance.
- There is an increasing need for policy frameworks that govern ownership, operation, airspace rights, and countermeasures to maintain societal trust.
- Digital literacy and ethical education must advance to help citizens understand the potential and limitations of drone and anti-drone technologies [76].
20. Concluding Remarks
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Python Code used for Bar-Graph Generation Jupyter Notebook

Appendix B. Code for ANOVA results: F-Values and P-Values

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