The Application and Effect of Artificial Intelligence in the Planning and Execution of the Events Industry

You are invited to participate in a survey for a Master’s dissertation research project at the University of Greenwich, titled The Application and Effect of Artificial Intelligence in the Planning and Execution of the Events Industry.


Research Purpose: This study explores the current adoption, practical effects, and implementation barriers of AI tools across event planning, on-site execution and risk management.


Participation Rule: Participation is completely voluntary. You may withdraw from the survey at any time without reason, with no negative consequences.


Confidentiality Statement: All responses are collected anonymously. No personally identifiable information will be recorded, and all data will only be used for academic research. Raw survey data will be securely stored and permanently destroyed after the dissertation is completed.


Estimated Completion Time: 5–8 minutes


By proceeding to complete the survey, you confirm that you are over 18 years old, have read and understood the above information, and agree to participate voluntarily

*1. How many years of working experience do you have in the events industry?
*2. What is your primary job role?
*3. What type of events does your organisation mainly focus on?
Exhibitions / trade shows (MICE)
Music festivals / cultural events
Sports events
Corporate conferences / seminars
Social gatherings (weddings, galas etc.)
Community events
Other (please specify)
*4. What is the size of your organisation?
*5. Has your organisation used artificial intelligence (AI) tools in event planning or on-site execution? (If you select "No, and we have no plans to adopt AI tools", please skip directly to Section 4)
*6. Which of the following AI tools / technologies has your organisation applied in event management?
Recommendation engines / personalised agenda systems
AI chatbots / virtual assistants
Intelligent scheduling / resource allocation tools
Predictive analytics models (risk identification and crisis early warning)
Computer vision / crowd monitoring systems
AI hyperlocal weather forecasting tools
Social media sentiment analysis tools
AI audience segmentation / marketing optimisation tools
Generative AI (LLMs) for content creation
Other (please specify)
*7. AI tools have improved the personalisation of event content and agendas for attendees (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*8. AI-powered communication (e.g. chatbots) has increased the efficiency of attendee query responses (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*9. Overall, AI applications have improved attendee satisfaction with events (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*10. AI tools have reduced time spent on routine event planning work (scheduling, vendor management etc.) (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*11. AI has optimised the allocation of on-site resources (staffing, catering, venue space etc.) (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*12. AI applications have reduced the overall operational cost of events (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*13. Compared with manual management, AI predictive models can identify crowd safety risks (overcrowding, abnormal gathering etc.) earlier (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*14. AI weather forecasting tools improve response capacity for extreme weather risks at outdoor events (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*15. Social media sentiment analysis can effectively detect negative public opinion crises in advance (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*16. AI risk monitoring tools have reduced the frequency of on-site safety emergencies (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*17. AI has improved the response speed of event teams to unexpected crises (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*18. AI audience segmentation has improved the conversion rate of event marketing campaigns (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*19. AI tools help to more accurately match target audiences with event content (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*20. AI-driven data analysis has improved post-event marketing evaluation efficiency (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*21. High upfront investment and implementation costs of enterprise-grade AI systems (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*22. Concerns about data privacy and compliance with regulations (e.g. GDPR) (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*23. Lack of technical literacy and AI-related skills among event staff (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*24. Resistance to technological change from existing employees (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*25. Uncertain reliability and accuracy of AI prediction results (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*26. Risk of over-reliance on AI systems and erosion of human judgment (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*27. Lack of event-specific AI solutions suitable for small and medium enterprises (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)
*28. Concerns about cybersecurity risks brought by AI data collection (1 = Strongly Disagree | 2 = Disagree | 3 = Neutral | 4 = Agree | 5 = Strongly Agree)