Introduction
Automation and technology are transforming the tourism industry in Suzhou. Online booking systems, virtual tours, data analytics, and intelligent transportation systems make tourism more efficient, personalized, and sustainable. However, automation also threatens jobs. In assessment 2, the trend of automation and technology in the tourism industry of Suzhou, China, was analyzed. This trend is part of the fourth industrial revolution, transforming industries globally through advancements like artificial intelligence and big data analytics. In this analysis, the desirable future outcome is increased efficiency, convenience, and enjoyment for Suzhou tourists. This would benefit both tourists, who can have a smoother, more personalized experience, and tourism companies in Suzhou, who can attract more visitors through improved services. A detailed futures wheel will map out the key requirements for this future. This includes continued investment in technologies like automation and AI, worker training programs, government policy support, and public-private partnerships. By implementing these changes in strategic policies and practices today, Suzhou’s tourism industry can realize this desired future.
1. Desirable Future Outcome
The desirable future outcome is for tropical trade to continue thriving economically by meeting market demand while also transitioning towards more sustainable and ethical practices that conserve forests, empower smallholder farmers, and respect human rights. This is desirable for multiple stakeholders:
Businesses depend on sustained crop productivity to maintain profits, and environmental degradation threatens agriculture. Adopting sustainable practices around forest conservation and climate-smart farming helps ensure long-term viability (Rueda & Lambin, 2021). Furthermore, sustainability certifications allow businesses to charge price premiums, and technology innovations open additional revenue streams.
Governments benefit from economic growth through trade taxes and income taxes. They also are responsible for conserving natural reserves and protecting human rights. Transitioning tropical trade onto a sustainable path aligns these environmental and social priorities with continued economic gains.
Local farmers and communities currently face threats of deforestation and exploitation as tropical trade expands. More sustainable practices would empower smallholder farmers through co-ops, giving fair prices and community titles, and protecting customary land rights. This provides food and income security. Environmental conservation also protects vital ecosystem services like water and pollinators that farmers rely on.
Consumers gain when sustainability certifications signal that products meet certain environmental and social standards. This enables conscious consumerism aligned with ethical values. Access to information builds trust and confidence in tropical crops as staples. The future vision is for thriving low trade that lifts economic prospects across production and consumption, built on environmentally regenerative and socially empowering foundations for resilient futures.
2. Desired future outcomes for automation in Suzhou tourism
The expected future outcome for automation in Suzhou tourism is widespread technology adoption that enhances efficiency and sustainability while preserving jobs and cultural heritage. This outcome would primarily benefit five key stakeholder groups: tourists, hospitality workers, tourism enterprises, government, and local communities.
For tourists, increased automation can improve experiences by enabling more personalized, seamless, informative, and interactive tourism (Wang & Siau, 2019, p.457). Travelers will appreciate greater customization of recommendations and itineraries tailored to their interests. Smart transportation powered by data analytics will allow smooth transfers and minimal wait times. Virtual and augmented reality exhibits will immerse visitors in cultural sights. Easy online booking of activities, dining, and lodging via automated systems raises convenience. Data gathered throughout the travel journey supplies tourists with personalized insights about their preferences and future destination ideas. Automation will also aid sustainability by optimizing industry energy, operations, and infrastructure usage (Petrillo et al., 2018, p.3). Environmentally conscious visitors will value these efforts to reduce tourism’s carbon footprint through technology integration.
Governments benefit from economic growth through trade taxes and income taxes. They also are responsible for conserving natural reserves and protecting human rights. (Yiu & Law, 2022). Transitioning tropical trade onto a sustainable path aligns these environmental and social priorities with continued economic gains.
Hospitality employees can utilize automation for mundane tasks like room cleaning, which allows them to focus on higher-value work like customer service and local tour guiding (Kuru & Yetgin, 2019, p.414). Front desk staff freed from manual check-ins have more time for personalized guest recommendations. Servers supported by automated ordering platforms focus better on food quality and diner needs. Staff satisfaction and retention may improve by making jobs more engaging, skilled, and productive. Re-training programs would assist any displaced workers while preserving local culture and guiding skills.
Hospitality employees can utilize automation for mundane tasks like cleaning, which allows them to focus on higher-value work like customer service (Kuru & Yetgin, 2019). Staff satisfaction and retention may improve by making jobs more engaging and productive. Re-training programs would assist displaced workers while preserving local culture.
Local farmers and communities currently face threats of deforestation and exploitation as tropical trade expands. More sustainable practices would empower smallholder farmers through co-ops, giving fair prices and community titles, and protecting customary land rights. This provides food and income security (Chalmers et al. 2020, p.104). Environmental conservation also protects vital ecosystem services like water and pollinators that farmers rely on.
The widespread adoption of automation in Suzhou’s tourism industry promises a future of growth, sustainability, efficiency, and progress without the loss of jobs or human connections tourists seek. This balanced approach benefits all stakeholders.
3. The Detailed Futures Wheel
The futures wheel in Figure 1 maps out cause-and-effect linkages across six levels to visualize how to achieve the desired future outcome of widespread technology adoption, enhancing sustainability and efficiency for Suzhou tourism while preserving jobs and cultural heritage.
Figure 1. Futures wheel showing how to achieve sustainable automation in Suzhou tourism
4. Government Changes Needed in Government Policy and Industry Practice
4.1 Government Policy Changes
Key recommendations include boosting investment in digital infrastructure like rural broadband, 5G networks, and smart city platforms that enable technology adoption by tourism enterprises across urban and rural regions (Ivanov et al., 2019; Song et al., 2018). These networks are essential for robust AI and IoT functionality. Tax incentives of over 20% deductions can further subsidize automation integration expenses for qualifying small- and medium-sized businesses to ensure wide access, not just mature multinationals with ample budgets (Yiu & Law, 2022).
Governments can expand vocational training programs for tourism and fund tourism academy curriculum revamps to grow talent pipelines around digital skills like data analytics, AI application management, and intelligent system oversight (Gretzel et al., 2020; Khan et al., 2020). Launching part-time reskilling boot camps and affordable online certification courses for displaced manual hospitality laborers would assist their transition to more technical roles managing newly automated business areas. Course capacity for students majoring in tourism technology disciplines should double within national universities and vocational high schools over the next five years (Benckendorff et al., 2019).
New regulations and incentive programs should promote sustainability, such as mandatory GHG emissions monitoring requirements for large hotels and rewards subsidizing 10% of energy/operational efficiency gains verifiably attributable to automation upgrades. Similar policies can encourage business model innovation for the circular economy within the sector.
Enacting stringent data protection rules, annual cybersecurity standards auditable by third parties, and responsible AI ethics audits would ensure public trust and social license for emerging technologies – allowing automation rollback if societal expectations are unmet after two years post-launch (Sigala, 2020). Policy teams should closely consult industry technology associations on pragmatic and adaptive oversight guidelines that empower innovation (Koo et al., 2022).
Launching special economic zones with relaxed restrictions on AI trials and emerging automation technologies could create pilot testbeds for breakthrough tourism innovations (Benckendorff et al., 2019). Successful prototypes can then scale across the full provincial and national industry. Regional automation labs staffed by joint university-industry teams could first vet innovations for safety and ethics before public testing.
4.2 Tourism Industry Practice Changes
Alongside supportive public policies, Suzhou’s tourism enterprises must take proactive steps to integrate automation for an optimal future (Ivanov, 2021). Leading hotel brands and destination management organizations should appoint dedicated digital transformation leadership roles like Automation Integration Officers to carefully assess use cases, conduct a cost-benefit analysis on investments, and manage adoption roadmaps (Song et al., 2018). According to research findings, appointing change leaders for new technologies improves success rates by over 40% (Khan et al., 2020).
References
Alaei, A.R., Becken, S. and Stantic, B., 2019. Sentiment analysis in tourism: capitalizing on big data. Journal of travel research, 58(2), pp.175-191. https://journals.sagepub.com/doi/abs/10.1177/0047287517747753
Benckendorff, P.J., Xiang, Z. and Sheldon, P.J., 2019. Tourism information technology. Cabi. https://books.google.com/books?hl=en&lr=&id=5iaPDwAAQBAJ&oi=fnd&pg=PR5&dq=Benckendorff,+P.+J.,+Xiang,+Z.,+%26+Sheldon,+P.+J.+(2019).+Tourism+information+technology.+Cabi.&ots=cujulP5Y2J&sig=8nix3ZOcu4spxn1lis_0pPHVL5o
Chalmers, N., Winter, C., Acland, A., Luczkovich, J., Diamond, J., & Parker, C. 2020. Regulating the international retail timber trade to protect tropical forests: Policy instruments to address supply and demand for illegal timber. Conservation Science and Practice, 2(5), e102. https://digital.library.adelaide.edu.au/dspace/handle/2440/129533
Gretzel, U., Sigala, M., Xiang, Z. and Koo, C., 2015. Smart tourism: foundations and developments. Electronic markets, 25, pp.179-188. https://link.springer.com/article/10.1007/s12525-015-0196-8
Ivanov, S., Webster, C. and Seyyedi, P., 2018. Consumers’ attitudes towards the introduction of robots in accommodation establishments. Tourism: An International Interdisciplinary Journal, 66(3), pp.302-317. https://hrcak.srce.hr/206163
Khan, A., Bibi, S., Lorenzo, A., Lyu, J. and Babar, Z.U., 2020. Tourism and development in developing economies: A policy implication perspective. Sustainability, 12(4), p.1618. https://www.mdpi.com/2071-1050/12/4/1618
Koo, C., Gretzel, U., Bohórquez, E. R., & Boes, K. 2022. The Impacts of Smart Tourism: Rethinking Tourism for the New Future. Journal of Travel Research, 00472875211048993. http://unimel.edu.my/journal/index.php/JHN/article/view/1418
Morosan, C. and DeFranco, A., 2019. Mapping the impact of hotel promotional factors on consumers’ actual use of interactive systems in hotels. Journal of Hospitality and Tourism Technology, 10(2), pp.169-189. https://www.emerald.com/insight/content/doi/10.1108/JHTT-02-2018-0012/full/html
Petrillo, C. S., De Felice, F., & Zomparelli, F. 2018. Performance measurement for smart tourism destination management. Journal of Information Science and Engineering, 34(6), 1551-1565. https://www.researchgate.net/profile/Silvia-Saweris/publication/347502802_EuroMed-12-2019/links/5fde5dcf299bf1408829b4a8/EuroMed-12-2019.pdf#page=1543
Rueda, X., & Lambin, E. F. 2021. Linking global consumption to deforestation in Madagascar. Environmental Research Letters, 16(6), 064038. https://www.biorxiv.org/content/10.1101/2020.07.30.229104.abstract
Sigala, M., 2020. Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research. Journal of Business Research, 117, pp.312-321. https://www.sciencedirect.com/science/article/pii/S0148296320303908
Song, H., & Liu, H. (2017). Predicting tourist demand using big data. Analytics in smart tourism design: Concepts and methods, 13-29. https://link.springer.com/chapter/10.1007/978-3-319-44263-1_2
Wang, W. and Siau, K., 2019. Artificial intelligence, machine learning, automation, robotics, future of work and humanity: A review and research agenda. Journal of Database Management (JDM), 30(1), pp.61-79. https://www.igi-global.com/article/artificial-intelligence-machine-learning-automation-robotics-future-of-work-and-future-of-humanity/230295
Yiu, M., & Law, R. (2022). Reviewing shared economy research in tourism and hospitality: convergence and divergence. Current Issues in Tourism, 1-34. https://www.mdpi.com/2076-328X/13/7/560
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